fix

Dockerfile

@@ -6,6 +6,5 @@ COPY . .
 ENV HF_HOME=/data/huggingface
 # Expose port
 EXPOSE 7860
-RUN pip install tenacity
 # Start the server
 CMD ["python", "/app/src/server/main.py", "--host", "0.0.0.0", "--port", "7860", "--config", "config_two"]

src/server/main-v2.py

@@ -0,0 +1,855 @@
+import argparse
+import io
+import os
+from time import time
+from typing import List, Dict
+import tempfile
+import uvicorn
+from fastapi import Depends, FastAPI, File, HTTPException, Query, Request, UploadFile, Body, Form, APIRouter
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import JSONResponse, RedirectResponse, StreamingResponse
+from PIL import Image
+from pydantic import BaseModel, field_validator
+from pydantic_settings import BaseSettings
+from slowapi import Limiter
+from slowapi.util import get_remote_address
+import torch
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoProcessor, BitsAndBytesConfig, AutoModel, Gemma3ForConditionalGeneration
+from IndicTransToolkit import IndicProcessor
+import json
+import asyncio
+from contextlib import asynccontextmanager
+import soundfile as sf
+import numpy as np
+import requests
+from starlette.responses import StreamingResponse
+from logging_config import logger
+from tts_config import SPEED, ResponseFormat, config as tts_config
+import torchaudio
+from tenacity import retry, stop_after_attempt, wait_exponential
+from torch.cuda.amp import autocast
+
+# Device setup
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.bfloat16 if device != "cpu" else torch.float32
+logger.info(f"{'GPU' if device != 'cpu' else 'CPU'} will be used for inference")
+
+# Check CUDA availability and version
+cuda_available = torch.cuda.is_available()
+cuda_version = torch.version.cuda if cuda_available else None
+if cuda_available:
+    device_idx = torch.cuda.current_device()
+    capability = torch.cuda.get_device_capability(device_idx)
+    compute_capability_float = float(f"{capability[0]}.{capability[1]}")
+    print(f"CUDA version: {cuda_version}")
+    print(f"CUDA Compute Capability: {compute_capability_float}")
+else:
+    print("CUDA is not available on this system.")
+
+# Settings
+class Settings(BaseSettings):
+    llm_model_name: str = "google/gemma-3-4b-it"
+    max_tokens: int = 512
+    host: str = "0.0.0.0"
+    port: int = 7860
+    chat_rate_limit: str = "100/minute"
+    speech_rate_limit: str = "5/minute"
+
+    @field_validator("chat_rate_limit", "speech_rate_limit")
+    def validate_rate_limit(cls, v):
+        if not v.count("/") == 1 or not v.split("/")[0].isdigit():
+            raise ValueError("Rate limit must be in format 'number/period' (e.g., '5/minute')")
+        return v
+
+    class Config:
+        env_file = ".env"
+
+settings = Settings()
+
+# Quantization config for LLM
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+# Request queue for concurrency control
+request_queue = asyncio.Queue(maxsize=10)
+
+# LLM Manager with batching
+class LLMManager:
+    def __init__(self, model_name: str, device: str = device):
+        self.model_name = model_name
+        self.device = torch.device(device)
+        self.torch_dtype = torch.bfloat16 if self.device.type != "cpu" else torch.float32
+        self.model = None
+        self.processor = None
+        self.is_loaded = False
+        self.token_cache = {}
+        self.load()
+        logger.info(f"LLMManager initialized with model {model_name} on {self.device}")
+
+    def load(self):
+        if not self.is_loaded:
+            try:
+                self.model = Gemma3ForConditionalGeneration.from_pretrained(
+                    self.model_name,
+                    device_map="auto",
+                    quantization_config=quantization_config,
+                    torch_dtype=self.torch_dtype
+                ).eval()
+                self.processor = AutoProcessor.from_pretrained(self.model_name)
+                dummy_input = self.processor("test", return_tensors="pt").to(self.device)
+                with torch.no_grad():
+                    self.model.generate(**dummy_input, max_new_tokens=10)
+                self.is_loaded = True
+                logger.info(f"LLM {self.model_name} loaded and warmed up on {self.device}")
+            except Exception as e:
+                logger.error(f"Failed to load LLM: {str(e)}")
+                raise
+
+    def unload(self):
+        if self.is_loaded:
+            del self.model
+            del self.processor
+            if self.device.type == "cuda":
+                torch.cuda.empty_cache()
+                logger.info(f"GPU memory allocated after unload: {torch.cuda.memory_allocated()}")
+            self.is_loaded = False
+            self.token_cache.clear()
+            logger.info(f"LLM {self.model_name} unloaded from {self.device}")
+
+    async def generate(self, prompt: str, max_tokens: int = settings.max_tokens, temperature: float = 0.7) -> str:
+        if not self.is_loaded:
+            self.load()
+
+        cache_key = f"{prompt}:{max_tokens}:{temperature}"
+        if cache_key in self.token_cache:
+            logger.info("Using cached response")
+            return self.token_cache[cache_key]
+
+        future = asyncio.Future()
+        await request_queue.put({"prompt": prompt, "max_tokens": max_tokens, "temperature": temperature, "future": future})
+        response = await future
+        self.token_cache[cache_key] = response
+        logger.info(f"Generated response: {response}")
+        return response
+
+    async def batch_generate(self, prompts: List[Dict]) -> List[str]:
+        messages_batch = [
+            [
+                {"role": "system", "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Answer questions considering India as base country and Karnataka as base state. Provide a concise response in one sentence maximum."}]},
+                {"role": "user", "content": [{"type": "text", "text": prompt["prompt"]}]}
+            ]
+            for prompt in prompts
+        ]
+        try:
+            inputs_vlm = self.processor.apply_chat_template(
+                messages_batch,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt",
+                padding=True
+            ).to(self.device, dtype=torch.bfloat16)
+
+            with autocast(), torch.no_grad():
+                outputs = self.model.generate(
+                    **inputs_vlm,
+                    max_new_tokens=max(prompt["max_tokens"] for prompt in prompts),
+                    do_sample=True,
+                    top_p=0.9,
+                    temperature=max(prompt["temperature"] for prompt in prompts)
+                )
+            responses = [
+                self.processor.decode(output[input_len:], skip_special_tokens=True)
+                for output, input_len in zip(outputs, inputs_vlm["input_ids"].shape[1])
+            ]
+            logger.info(f"Batch generated {len(responses)} responses")
+            return responses
+        except Exception as e:
+            logger.error(f"Error in batch generation: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Batch generation failed: {str(e)}")
+
+    async def vision_query(self, image: Image.Image, query: str) -> str:
+        if not self.is_loaded:
+            self.load()
+
+        messages_vlm = [
+            {"role": "system", "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Summarize your answer in maximum 1 sentence."}]},
+            {"role": "user", "content": [{"type": "text", "text": query}] + ([{"type": "image", "image": image}] if image and image.size[0] > 0 and image.size[1] > 0 else [])}
+        ]
+
+        try:
+            inputs_vlm = self.processor.apply_chat_template(
+                messages_vlm,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt"
+            ).to(self.device, dtype=torch.bfloat16)
+        except Exception as e:
+            logger.error(f"Error in apply_chat_template: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Failed to process input: {str(e)}")
+
+        input_len = inputs_vlm["input_ids"].shape[-1]
+        with torch.inference_mode():
+            generation = self.model.generate(**inputs_vlm, max_new_tokens=512, do_sample=True, temperature=0.7)
+            generation = generation[0][input_len:]
+        decoded = self.processor.decode(generation, skip_special_tokens=True)
+        logger.info(f"Vision query response: {decoded}")
+        return decoded
+
+    async def chat_v2(self, image: Image.Image, query: str) -> str:
+        if not self.is_loaded:
+            self.load()
+
+        messages_vlm = [
+            {"role": "system", "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Answer questions considering India as base country and Karnataka as base state."}]},
+            {"role": "user", "content": [{"type": "text", "text": query}] + ([{"type": "image", "image": image}] if image and image.size[0] > 0 and image.size[1] > 0 else [])}
+        ]
+
+        try:
+            inputs_vlm = self.processor.apply_chat_template(
+                messages_vlm,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt"
+            ).to(self.device, dtype=torch.bfloat16)
+        except Exception as e:
+            logger.error(f"Error in apply_chat_template: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Failed to process input: {str(e)}")
+
+        input_len = inputs_vlm["input_ids"].shape[-1]
+        with torch.inference_mode():
+            generation = self.model.generate(**inputs_vlm, max_new_tokens=512, do_sample=True, temperature=0.7)
+            generation = generation[0][input_len:]
+        decoded = self.processor.decode(generation, skip_special_tokens=True)
+        logger.info(f"Chat_v2 response: {decoded}")
+        return decoded
+
+# TTS Manager
+class TTSManager:
+    def __init__(self, device_type=device):
+        self.device_type = torch.device(device_type)
+        self.model = None
+        self.repo_id = "ai4bharat/IndicF5"
+        self.load()
+
+    def load(self):
+        if not self.model:
+            logger.info("Loading TTS model IndicF5...")
+            self.model = AutoModel.from_pretrained(self.repo_id, trust_remote_code=True).to(self.device_type)
+            logger.info("TTS model IndicF5 loaded")
+
+    def synthesize(self, text, ref_audio_path, ref_text):
+        if not self.model:
+            raise ValueError("TTS model not loaded")
+        with autocast():
+            return self.model(text, ref_audio_path=ref_audio_path, ref_text=ref_text)
+
+# Translation Manager
+class TranslateManager:
+    def __init__(self, src_lang, tgt_lang, device_type=device, use_distilled=True):
+        self.device_type = torch.device(device_type)
+        self.tokenizer = None
+        self.model = None
+        self.src_lang = src_lang
+        self.tgt_lang = tgt_lang
+        self.use_distilled = use_distilled
+        self.load()
+
+    def load(self):
+        if not self.tokenizer or not self.model:
+            if self.src_lang.startswith("eng") and not self.tgt_lang.startswith("eng"):
+                model_name = "ai4bharat/indictrans2-en-indic-dist-200M" if self.use_distilled else "ai4bharat/indictrans2-en-indic-1B"
+            elif not self.src_lang.startswith("eng") and self.tgt_lang.startswith("eng"):
+                model_name = "ai4bharat/indictrans2-indic-en-dist-200M" if self.use_distilled else "ai4bharat/indictrans2-indic-en-1B"
+            elif not self.src_lang.startswith("eng") and not self.tgt_lang.startswith("eng"):
+                model_name = "ai4bharat/indictrans2-indic-indic-dist-320M" if self.use_distilled else "ai4bharat/indictrans2-indic-indic-1B"
+            else:
+                raise ValueError("Invalid language combination")
+            self.tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+            self.model = AutoModelForSeq2SeqLM.from_pretrained(
+                model_name,
+                trust_remote_code=True,
+                torch_dtype=torch.float16,
+                attn_implementation="flash_attention_2"
+            ).to(self.device_type)
+            self.model = torch.compile(self.model, mode="reduce-overhead")
+            logger.info(f"Translation model {model_name} loaded")
+
+# Model Manager
+class ModelManager:
+    def __init__(self, device_type=device, use_distilled=True, is_lazy_loading=False):
+        self.models = {}
+        self.device_type = device_type
+        self.use_distilled = use_distilled
+        self.is_lazy_loading = is_lazy_loading
+
+    def load_model(self, src_lang, tgt_lang, key):
+        logger.info(f"Loading translation model for {src_lang} -> {tgt_lang}")
+        translate_manager = TranslateManager(src_lang, tgt_lang, self.device_type, self.use_distilled)
+        self.models[key] = translate_manager
+        logger.info(f"Loaded translation model for {key}")
+
+    def get_model(self, src_lang, tgt_lang):
+        key = self._get_model_key(src_lang, tgt_lang)
+        if key not in self.models and self.is_lazy_loading:
+            self.load_model(src_lang, tgt_lang, key)
+        return self.models.get(key) or (self.load_model(src_lang, tgt_lang, key) or self.models[key])
+
+    def _get_model_key(self, src_lang, tgt_lang):
+        if src_lang.startswith("eng") and not tgt_lang.startswith("eng"):
+            return 'eng_indic'
+        elif not src_lang.startswith("eng") and tgt_lang.startswith("eng"):
+            return 'indic_eng'
+        elif not src_lang.startswith("eng") and not tgt_lang.startswith("eng"):
+            return 'indic_indic'
+        raise ValueError("Invalid language combination")
+
+# ASR Manager
+class ASRModelManager:
+    def __init__(self, device_type=device):
+        self.device_type = torch.device(device_type)
+        self.model = None
+        self.model_language = {"kannada": "kn"}
+        self.load()
+
+    def load(self):
+        if not self.model:
+            logger.info("Loading ASR model...")
+            self.model = AutoModel.from_pretrained(
+                "ai4bharat/indic-conformer-600m-multilingual",
+                trust_remote_code=True
+            ).to(self.device_type)
+            logger.info("ASR model loaded")
+
+# Global Managers
+llm_manager = LLMManager(settings.llm_model_name)
+model_manager = ModelManager()
+asr_manager = ASRModelManager()
+tts_manager = TTSManager()
+ip = IndicProcessor(inference=True)
+
+# TTS Constants
+EXAMPLES = [
+    {
+        "audio_name": "KAN_F (Happy)",
+        "audio_url": "https://github.com/AI4Bharat/IndicF5/raw/refs/heads/main/prompts/KAN_F_HAPPY_00001.wav",
+        "ref_text": "ನಮ್‌ ಫ್ರಿಜ್ಜಲ್ಲಿ ಕೂಲಿಂಗ್‌ ಸಮಸ್ಯೆ ಆಗಿ ನಾನ್‌ ಭಾಳ ದಿನದಿಂದ ಒದ್ದಾಡ್ತಿದ್ದೆ, ಆದ್ರೆ ಅದ್ನೀಗ ಮೆಕಾನಿಕ್ ಆಗಿರೋ ನಿಮ್‌ ಸಹಾಯ್ದಿಂದ ಬಗೆಹರಿಸ್ಕೋಬೋದು ಅಂತಾಗಿ ನಿರಾಳ ಆಯ್ತು ನಂಗೆ।",
+        "synth_text": "ಚೆನ್ನೈನ ಶೇರ್ ಆಟೋ ಪ್ರಯಾಣಿಕರ ನಡುವೆ ಆಹಾರವನ್ನು ಹಂಚಿಕೊಂಡು ತಿನ್ನುವುದು ನನಗೆ ಮನಸ್ಸಿಗೆ ತುಂಬಾ ಒಳ್ಳೆಯದೆನಿಸುವ ವಿಷಯ."
+    },
+]
+
+# Pydantic Models
+class SynthesizeRequest(BaseModel):
+    text: str
+    ref_audio_name: str
+    ref_text: str = None
+
+class KannadaSynthesizeRequest(BaseModel):
+    text: str
+
+class ChatRequest(BaseModel):
+    prompt: str
+    src_lang: str = "kan_Knda"
+    tgt_lang: str = "kan_Knda"
+
+    @field_validator("prompt")
+    def prompt_must_be_valid(cls, v):
+        if len(v) > 1000:
+            raise ValueError("Prompt cannot exceed 1000 characters")
+        return v.strip()
+
+    @field_validator("src_lang", "tgt_lang")
+    def validate_language(cls, v):
+        if v not in SUPPORTED_LANGUAGES:
+            raise ValueError(f"Unsupported language code: {v}. Supported codes: {', '.join(SUPPORTED_LANGUAGES)}")
+        return v
+
+class ChatResponse(BaseModel):
+    response: str
+
+class TranslationRequest(BaseModel):
+    sentences: List[str]
+    src_lang: str
+    tgt_lang: str
+
+class TranscriptionResponse(BaseModel):
+    text: str
+
+class TranslationResponse(BaseModel):
+    translations: List[str]
+
+# TTS Functions
+@retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=10))
+def load_audio_from_url(url: str):
+    response = requests.get(url)
+    if response.status_code == 200:
+        audio_data, sample_rate = sf.read(io.BytesIO(response.content))
+        return sample_rate, audio_data
+    raise HTTPException(status_code=500, detail="Failed to load reference audio from URL after retries")
+
+async def synthesize_speech(tts_manager: TTSManager, text: str, ref_audio_name: str, ref_text: str) -> io.BytesIO:
+    async with request_queue:
+        ref_audio_url = next((ex["audio_url"] for ex in EXAMPLES if ex["audio_name"] == ref_audio_name), None)
+        if not ref_audio_url:
+            raise HTTPException(status_code=400, detail="Invalid reference audio name.")
+        if not text.strip() or not ref_text.strip():
+            raise HTTPException(status_code=400, detail="Text or reference text cannot be empty.")
+
+        logger.info(f"Synthesizing speech for text: {text[:50]}... with ref_audio: {ref_audio_name}")
+        loop = asyncio.get_running_loop()
+        sample_rate, audio_data = await loop.run_in_executor(None, load_audio_from_url, ref_audio_url)
+
+        with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_audio:
+            await loop.run_in_executor(None, sf.write, temp_audio.name, audio_data, sample_rate, "WAV")
+            temp_audio.flush()
+            audio = tts_manager.synthesize(text, temp_audio.name, ref_text)
+
+        buffer = io.BytesIO()
+        await loop.run_in_executor(None, sf.write, buffer, audio.astype(np.float32) / 32768.0 if audio.dtype == np.int16 else audio, 24000, "WAV")
+        buffer.seek(0)
+        logger.info("Speech synthesis completed")
+        return buffer
+
+# Supported Languages
+SUPPORTED_LANGUAGES = {
+    "asm_Beng", "kas_Arab", "pan_Guru", "ben_Beng", "kas_Deva", "san_Deva",
+    "brx_Deva", "mai_Deva", "sat_Olck", "doi_Deva", "mal_Mlym", "snd_Arab",
+    "eng_Latn", "mar_Deva", "snd_Deva", "gom_Deva", "mni_Beng", "tam_Taml",
+    "guj_Gujr", "mni_Mtei", "tel_Telu", "hin_Deva", "npi_Deva", "urd_Arab",
+    "kan_Knda", "ory_Orya",
+    "deu_Latn", "fra_Latn", "nld_Latn", "spa_Latn", "ita_Latn",
+    "por_Latn", "rus_Cyrl", "pol_Latn"
+}
+
+# Dependency
+def get_translate_manager(src_lang: str, tgt_lang: str) -> TranslateManager:
+    return model_manager.get_model(src_lang, tgt_lang)
+
+# Translation Function
+async def perform_internal_translation(sentences: List[str], src_lang: str, tgt_lang: str) -> List[str]:
+    try:
+        translate_manager = model_manager.get_model(src_lang, tgt_lang)
+    except ValueError as e:
+        logger.info(f"Model not preloaded: {str(e)}, loading now...")
+        key = model_manager._get_model_key(src_lang, tgt_lang)
+        model_manager.load_model(src_lang, tgt_lang, key)
+        translate_manager = model_manager.get_model(src_lang, tgt_lang)
+
+    if not translate_manager.model:
+        translate_manager.load()
+
+    batch = ip.preprocess_batch(sentences, src_lang=src_lang, tgt_lang=tgt_lang)
+    inputs = translate_manager.tokenizer(batch, truncation=True, padding="longest", return_tensors="pt", return_attention_mask=True).to(translate_manager.device_type)
+    with torch.no_grad(), autocast():
+        generated_tokens = translate_manager.model.generate(**inputs, use_cache=True, min_length=0, max_length=256, num_beams=5, num_return_sequences=1)
+    with translate_manager.tokenizer.as_target_tokenizer():
+        generated_tokens = translate_manager.tokenizer.batch_decode(generated_tokens.detach().cpu().tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    return ip.postprocess_batch(generated_tokens, lang=tgt_lang)
+
+# Lifespan Event Handler
+translation_configs = []
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    def load_all_models():
+        logger.info("Loading LLM model...")
+        llm_manager.load()
+        logger.info("Loading TTS model...")
+        tts_manager.load()
+        logger.info("Loading ASR model...")
+        asr_manager.load()
+        translation_tasks = [
+            ('eng_Latn', 'kan_Knda', 'eng_indic'),
+            ('kan_Knda', 'eng_Latn', 'indic_eng'),
+            ('kan_Knda', 'hin_Deva', 'indic_indic'),
+        ]
+        for config in translation_configs:
+            src_lang = config["src_lang"]
+            tgt_lang = config["tgt_lang"]
+            key = model_manager._get_model_key(src_lang, tgt_lang)
+            translation_tasks.append((src_lang, tgt_lang, key))
+        for src_lang, tgt_lang, key in translation_tasks:
+            logger.info(f"Loading translation model for {src_lang} -> {tgt_lang}...")
+            model_manager.load_model(src_lang, tgt_lang, key)
+        logger.info("All models loaded successfully")
+
+    logger.info("Starting sequential model loading...")
+    load_all_models()
+    batch_task = asyncio.create_task(batch_worker())
+    yield
+    batch_task.cancel()
+    llm_manager.unload()
+    logger.info("Server shutdown complete")
+
+# Batch Worker
+async def batch_worker():
+    while True:
+        batch = []
+        last_request_time = time()
+        try:
+            while len(batch) < 4:
+                try:
+                    request = await asyncio.wait_for(request_queue.get(), timeout=1.0)
+                    batch.append(request)
+                    current_time = time()
+                    if current_time - last_request_time > 1.0 and batch:
+                        break
+                    last_request_time = current_time
+                except asyncio.TimeoutError:
+                    if batch:
+                        break
+                    continue
+            if batch:
+                start_time = time()
+                responses = await llm_manager.batch_generate(batch)
+                duration = time() - start_time
+                logger.info(f"Batch of {len(batch)} requests processed in {duration:.3f} seconds")
+                for request, response in zip(batch, responses):
+                    request["future"].set_result(response)
+        except Exception as e:
+            logger.error(f"Batch worker error: {str(e)}")
+            for request in batch:
+                request["future"].set_exception(e)
+
+# FastAPI App
+app = FastAPI(
+    title="Dhwani API",
+    description="AI Chat API supporting Indian languages",
+    version="1.0.0",
+    redirect_slashes=False,
+    lifespan=lifespan
+)
+
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=False,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.middleware("http")
+async def add_request_timing(request: Request, call_next):
+    start_time = time()
+    response = await call_next(request)
+    duration = time() - start_time
+    logger.info(f"Request to {request.url.path} took {duration:.3f} seconds")
+    response.headers["X-Response-Time"] = f"{duration:.3f}"
+    return response
+
+limiter = Limiter(key_func=get_remote_address)
+app.state.limiter = limiter
+
+# Endpoints
+@app.post("/audio/speech", response_class=StreamingResponse)
+async def synthesize_kannada(request: KannadaSynthesizeRequest):
+    if not tts_manager.model:
+        raise HTTPException(status_code=503, detail="TTS model not loaded")
+    kannada_example = next(ex for ex in EXAMPLES if ex["audio_name"] == "KAN_F (Happy)")
+    if not request.text.strip():
+        raise HTTPException(status_code=400, detail="Text to synthesize cannot be empty.")
+    audio_buffer = await synthesize_speech(tts_manager, request.text, "KAN_F (Happy)", kannada_example["ref_text"])
+    return StreamingResponse(
+        audio_buffer,
+        media_type="audio/wav",
+        headers={"Content-Disposition": "attachment; filename=synthesized_kannada_speech.wav"}
+    )
+
+@app.post("/translate", response_model=TranslationResponse)
+async def translate(request: TranslationRequest, translate_manager: TranslateManager = Depends(get_translate_manager)):
+    if not request.sentences:
+        raise HTTPException(status_code=400, detail="Input sentences are required")
+    batch = ip.preprocess_batch(request.sentences, src_lang=request.src_lang, tgt_lang=request.tgt_lang)
+    inputs = translate_manager.tokenizer(batch, truncation=True, padding="longest", return_tensors="pt", return_attention_mask=True).to(translate_manager.device_type)
+    with torch.no_grad(), autocast():
+        generated_tokens = translate_manager.model.generate(**inputs, use_cache=True, min_length=0, max_length=256, num_beams=5, num_return_sequences=1)
+    with translate_manager.tokenizer.as_target_tokenizer():
+        generated_tokens = translate_manager.tokenizer.batch_decode(generated_tokens.detach().cpu().tolist(), skip_special_tokens=True, clean_up_tokenization_spaces=True)
+    translations = ip.postprocess_batch(generated_tokens, lang=request.tgt_lang)
+    return TranslationResponse(translations=translations)
+
+@app.get("/v1/health")
+async def health_check():
+    memory_usage = torch.cuda.memory_allocated() / (24 * 1024**3) if cuda_available else 0
+    llm_status = "unhealthy"
+    llm_latency = None
+    if llm_manager.is_loaded:
+        start = time()
+        try:
+            llm_test = await llm_manager.generate("What is the capital of Karnataka?", max_tokens=10)
+            llm_latency = time() - start
+            llm_status = "healthy" if llm_test else "unhealthy"
+        except Exception as e:
+            logger.error(f"LLM health check failed: {str(e)}")
+
+    tts_status = "unhealthy"
+    tts_latency = None
+    if tts_manager.model:
+        start = time()
+        try:
+            audio_buffer = await synthesize_speech(tts_manager, "Test", "KAN_F (Happy)", EXAMPLES[0]["ref_text"])
+            tts_latency = time() - start
+            tts_status = "healthy" if audio_buffer else "unhealthy"
+        except Exception as e:
+            logger.error(f"TTS health check failed: {str(e)}")
+
+    asr_status = "unhealthy"
+    asr_latency = None
+    if asr_manager.model:
+        start = time()
+        try:
+            dummy_audio = np.zeros(16000, dtype=np.float32)
+            wav = torch.tensor(dummy_audio).unsqueeze(0).to(device)
+            with autocast(), torch.no_grad():
+                asr_test = asr_manager.model(wav, asr_manager.model_language["kannada"], "rnnt")
+            asr_latency = time() - start
+            asr_status = "healthy" if asr_test else "unhealthy"
+        except Exception as e:
+            logger.error(f"ASR health check failed: {str(e)}")
+
+    status = {
+        "status": "healthy" if llm_status == "healthy" and tts_status == "healthy" and asr_status == "healthy" else "degraded",
+        "model": settings.llm_model_name,
+        "llm_status": llm_status,
+        "llm_latency": f"{llm_latency:.3f}s" if llm_latency else "N/A",
+        "tts_status": tts_status,
+        "tts_latency": f"{tts_latency:.3f}s" if tts_latency else "N/A",
+        "asr_status": asr_status,
+        "asr_latency": f"{asr_latency:.3f}s" if asr_latency else "N/A",
+        "gpu_memory_usage": f"{memory_usage:.2%}"
+    }
+    logger.info("Health check completed")
+    return status
+
+@app.get("/")
+async def home():
+    return RedirectResponse(url="/docs")
+
+@app.post("/v1/unload_all_models")
+async def unload_all_models():
+    try:
+        logger.info("Starting to unload all models...")
+        llm_manager.unload()
+        logger.info("All models unloaded successfully")
+        return {"status": "success", "message": "All models unloaded"}
+    except Exception as e:
+        logger.error(f"Error unloading models: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to unload models: {str(e)}")
+
+@app.post("/v1/load_all_models")
+async def load_all_models():
+    try:
+        logger.info("Starting to load all models...")
+        llm_manager.load()
+        logger.info("All models loaded successfully")
+        return {"status": "success", "message": "All models loaded"}
+    except Exception as e:
+        logger.error(f"Error loading models: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to load models: {str(e)}")
+
+@app.post("/v1/translate", response_model=TranslationResponse)
+async def translate_endpoint(request: TranslationRequest):
+    logger.info(f"Received translation request: {request.dict()}")
+    try:
+        translations = await perform_internal_translation(request.sentences, request.src_lang, request.tgt_lang)
+        logger.info(f"Translation successful: {translations}")
+        return TranslationResponse(translations=translations)
+    except Exception as e:
+        logger.error(f"Unexpected error during translation: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Translation failed: {str(e)}")
+
+@app.post("/v1/chat", response_model=ChatResponse)
+@limiter.limit(settings.chat_rate_limit)
+async def chat(request: Request, chat_request: ChatRequest):
+    if not chat_request.prompt:
+        raise HTTPException(status_code=400, detail="Prompt cannot be empty")
+    logger.info(f"Received prompt: {chat_request.prompt}, src_lang: {chat_request.src_lang}, tgt_lang: {chat_request.tgt_lang}")
+    EUROPEAN_LANGUAGES = {"deu_Latn", "fra_Latn", "nld_Latn", "spa_Latn", "ita_Latn", "por_Latn", "rus_Cyrl", "pol_Latn"}
+    try:
+        if chat_request.src_lang != "eng_Latn" and chat_request.src_lang not in EUROPEAN_LANGUAGES:
+            translated_prompt = await perform_internal_translation([chat_request.prompt], chat_request.src_lang, "eng_Latn")
+            prompt_to_process = translated_prompt[0]
+            logger.info(f"Translated prompt to English: {prompt_to_process}")
+        else:
+            prompt_to_process = chat_request.prompt
+            logger.info("Prompt in English or European language, no translation needed")
+
+        response = await llm_manager.generate(prompt_to_process, settings.max_tokens)
+        logger.info(f"Generated English response: {response}")
+
+        if chat_request.tgt_lang != "eng_Latn" and chat_request.tgt_lang not in EUROPEAN_LANGUAGES:
+            translated_response = await perform_internal_translation([response], "eng_Latn", chat_request.tgt_lang)
+            final_response = translated_response[0]
+            logger.info(f"Translated response to {chat_request.tgt_lang}: {final_response}")
+        else:
+            final_response = response
+            logger.info(f"Response in {chat_request.tgt_lang}, no translation needed")
+        return ChatResponse(response=final_response)
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+@app.post("/v1/visual_query/")
+async def visual_query(
+    file: UploadFile = File(...),
+    query: str = Body(...),
+    src_lang: str = Query("kan_Knda", enum=list(SUPPORTED_LANGUAGES)),
+    tgt_lang: str = Query("kan_Knda", enum=list(SUPPORTED_LANGUAGES)),
+):
+    try:
+        image = Image.open(file.file)
+        if image.size == (0, 0):
+            raise HTTPException(status_code=400, detail="Uploaded image is empty or invalid")
+        if src_lang != "eng_Latn":
+            translated_query = await perform_internal_translation([query], src_lang, "eng_Latn")
+            query_to_process = translated_query[0]
+            logger.info(f"Translated query to English: {query_to_process}")
+        else:
+            query_to_process = query
+            logger.info("Query already in English, no translation needed")
+        answer = await llm_manager.vision_query(image, query_to_process)
+        logger.info(f"Generated English answer: {answer}")
+        if tgt_lang != "eng_Latn":
+            translated_answer = await perform_internal_translation([answer], "eng_Latn", tgt_lang)
+            final_answer = translated_answer[0]
+            logger.info(f"Translated answer to {tgt_lang}: {final_answer}")
+        else:
+            final_answer = answer
+            logger.info("Answer kept in English, no translation needed")
+        return {"answer": final_answer}
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+@app.post("/v1/chat_v2", response_model=ChatResponse)
+@limiter.limit(settings.chat_rate_limit)
+async def chat_v2(
+    request: Request,
+    prompt: str = Form(...),
+    image: UploadFile = File(default=None),
+    src_lang: str = Form("kan_Knda"),
+    tgt_lang: str = Form("kan_Knda"),
+):
+    if not prompt:
+        raise HTTPException(status_code=400, detail="Prompt cannot be empty")
+    if src_lang not in SUPPORTED_LANGUAGES or tgt_lang not in SUPPORTED_LANGUAGES:
+        raise HTTPException(status_code=400, detail=f"Unsupported language code. Supported codes: {', '.join(SUPPORTED_LANGUAGES)}")
+    logger.info(f"Received prompt: {prompt}, src_lang: {src_lang}, tgt_lang: {tgt_lang}, Image provided: {image is not None}")
+    try:
+        if image:
+            image_data = await image.read()
+            if not image_data:
+                raise HTTPException(status_code=400, detail="Uploaded image is empty")
+            img = Image.open(io.BytesIO(image_data))
+            if src_lang != "eng_Latn":
+                translated_prompt = await perform_internal_translation([prompt], src_lang, "eng_Latn")
+                prompt_to_process = translated_prompt[0]
+                logger.info(f"Translated prompt to English: {prompt_to_process}")
+            else:
+                prompt_to_process = prompt
+            decoded = await llm_manager.chat_v2(img, prompt_to_process)
+            logger.info(f"Generated English response: {decoded}")
+            if tgt_lang != "eng_Latn":
+                translated_response = await perform_internal_translation([decoded], "eng_Latn", tgt_lang)
+                final_response = translated_response[0]
+                logger.info(f"Translated response to {tgt_lang}: {final_response}")
+            else:
+                final_response = decoded
+        else:
+            if src_lang != "eng_Latn":
+                translated_prompt = await perform_internal_translation([prompt], src_lang, "eng_Latn")
+                prompt_to_process = translated_prompt[0]
+                logger.info(f"Translated prompt to English: {prompt_to_process}")
+            else:
+                prompt_to_process = prompt
+            decoded = await llm_manager.generate(prompt_to_process, settings.max_tokens)
+            logger.info(f"Generated English response: {decoded}")
+            if tgt_lang != "eng_Latn":
+                translated_response = await perform_internal_translation([decoded], "eng_Latn", tgt_lang)
+                final_response = translated_response[0]
+                logger.info(f"Translated response to {tgt_lang}: {final_response}")
+            else:
+                final_response = decoded
+        return ChatResponse(response=final_response)
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+@app.post("/transcribe/", response_model=TranscriptionResponse)
+async def transcribe_audio(file: UploadFile = File(...), language: str = Query(..., enum=list(asr_manager.model_language.keys()))):
+    if not asr_manager.model:
+        raise HTTPException(status_code=503, detail="ASR model not loaded")
+    try:
+        wav, sr = torchaudio.load(file.file, backend="cuda" if cuda_available else "cpu")
+        wav = torch.mean(wav, dim=0, keepdim=True).to(device)
+        target_sample_rate = 16000
+        if sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate).to(device)
+            wav = resampler(wav)
+        with autocast(), torch.no_grad():
+            transcription_rnnt = asr_manager.model(wav, asr_manager.model_language[language], "rnnt")
+        return TranscriptionResponse(text=transcription_rnnt)
+    except Exception as e:
+        logger.error(f"Error in transcription: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")
+
+@app.post("/v1/speech_to_speech")
+async def speech_to_speech(
+    request: Request,
+    file: UploadFile = File(...),
+    language: str = Query(..., enum=list(asr_manager.model_language.keys())),
+) -> StreamingResponse:
+    if not tts_manager.model:
+        raise HTTPException(status_code=503, detail="TTS model not loaded")
+    transcription = await transcribe_audio(file, language)
+    logger.info(f"Transcribed text: {transcription.text}")
+    chat_request = ChatRequest(prompt=transcription.text, src_lang=LANGUAGE_TO_SCRIPT.get(language, "kan_Knda"), tgt_lang=LANGUAGE_TO_SCRIPT.get(language, "kan_Knda"))
+    processed_text = await chat(request, chat_request)
+    logger.info(f"Processed text: {processed_text.response}")
+    voice_request = KannadaSynthesizeRequest(text=processed_text.response)
+    audio_response = await synthesize_kannada(voice_request)
+    return audio_response
+
+LANGUAGE_TO_SCRIPT = {"kannada": "kan_Knda"}
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Run the FastAPI server.")
+    parser.add_argument("--port", type=int, default=settings.port, help="Port to run the server on.")
+    parser.add_argument("--host", type=str, default=settings.host, help="Host to run the server on.")
+    parser.add_argument("--config", type=str, default="config_one", help="Configuration to use")
+    args = parser.parse_args()
+
+    def load_config(config_path="dhwani_config.json"):
+        with open(config_path, "r") as f:
+            return json.load(f)
+
+    config_data = load_config()
+    if args.config not in config_data["configs"]:
+        raise ValueError(f"Invalid config: {args.config}. Available: {list(config_data['configs'].keys())}")
+    
+    selected_config = config_data["configs"][args.config]
+    global_settings = config_data["global_settings"]
+
+    settings.llm_model_name = selected_config["components"]["LLM"]["model"]
+    settings.max_tokens = selected_config["components"]["LLM"]["max_tokens"]
+    settings.host = global_settings["host"]
+    settings.port = global_settings["port"]
+    settings.chat_rate_limit = global_settings["chat_rate_limit"]
+    settings.speech_rate_limit = global_settings["speech_rate_limit"]
+
+    llm_manager = LLMManager(settings.llm_model_name)
+    if selected_config["components"]["ASR"]:
+        asr_manager.model_language[selected_config["language"]] = selected_config["components"]["ASR"]["language_code"]
+    if selected_config["components"]["Translation"]:
+        translation_configs.extend(selected_config["components"]["Translation"])
+
+    host = args.host if args.host != settings.host else settings.host
+    port = args.port if args.port != settings.port else settings.port
+
+    uvicorn.run(app, host=host, port=port, workers=2)

src/server/main.py

@@ -1,11 +1,11 @@
 import argparse
 import io
 import os
-import tempfile
 from time import time
 from typing import List
+import tempfile
 import uvicorn
-from fastapi import Depends, FastAPI, File, HTTPException, Query, Request, UploadFile, Body, Form, APIRouter
+from fastapi import Depends, FastAPI, File, HTTPException, Query, Request, UploadFile, Body, Form
 from fastapi.middleware.cors import CORSMiddleware
 from fastapi.responses import JSONResponse, RedirectResponse, StreamingResponse
 from PIL import Image
@@ -14,7 +14,7 @@ from pydantic_settings import BaseSettings
 from slowapi import Limiter
 from slowapi.util import get_remote_address
 import torch
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoProcessor, AutoModel, Gemma3ForConditionalGeneration
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, AutoProcessor, BitsAndBytesConfig, AutoModel, Gemma3ForConditionalGeneration
 from IndicTransToolkit import IndicProcessor
 import json
 import asyncio
@@ -22,28 +22,32 @@ from contextlib import asynccontextmanager
 import soundfile as sf
 import numpy as np
 import requests
-import logging
 from starlette.responses import StreamingResponse
-from logging_config import logger  # Assumed external logging config
-from tts_config import SPEED, ResponseFormat, config as tts_config  # Assumed external TTS config
+from logging_config import logger
+from tts_config import SPEED, ResponseFormat, config as tts_config
 import torchaudio
-from tenacity import retry, stop_after_attempt, wait_exponential
-from torch.cuda.amp import autocast
 
 # Device setup
-device = "cuda:0" if torch.cuda.is_available() else "cpu"
-torch_dtype = torch.float16 if device != "cpu" else torch.float32  # Use float16 for speed
-logger.info(f"Using device: {device} with dtype: {torch_dtype}")
+if torch.cuda.is_available():
+    device = "cuda:0"
+    logger.info("GPU will be used for inference")
+else:
+    device = "cpu"
+    logger.info("CPU will be used for inference")
+torch_dtype = torch.bfloat16 if device != "cpu" else torch.float32
 
 # Check CUDA availability and version
 cuda_available = torch.cuda.is_available()
 cuda_version = torch.version.cuda if cuda_available else None
-if cuda_available:
+
+if torch.cuda.is_available():
     device_idx = torch.cuda.current_device()
     capability = torch.cuda.get_device_capability(device_idx)
-    logger.info(f"CUDA version: {cuda_version}, Compute Capability: {capability[0]}.{capability[1]}")
+    compute_capability_float = float(f"{capability[0]}.{capability[1]}")
+    print(f"CUDA version: {cuda_version}")
+    print(f"CUDA Compute Capability: {compute_capability_float}")
 else:
-    logger.info("CUDA is not available; falling back to CPU.")
+    print("CUDA is not available on this system.")
 
 # Settings
 class Settings(BaseSettings):
@@ -65,49 +69,41 @@ class Settings(BaseSettings):
 
 settings = Settings()
 
-# Request queue for concurrency control (max 10 concurrent GPU tasks)
-request_queue = asyncio.Queue(maxsize=10)
-
-# Logging optimization
-logging.basicConfig(level=os.getenv("LOG_LEVEL", "INFO"))
+# Quantization config for LLM
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
 
-# LLM Manager with persistent loading and improved caching
+# LLM Manager
 class LLMManager:
-    def __init__(self, model_name: str, device: str = device):
+    def __init__(self, model_name: str, device: str = "cuda" if torch.cuda.is_available() else "cpu"):
         self.model_name = model_name
         self.device = torch.device(device)
-        self.torch_dtype = torch_dtype
+        self.torch_dtype = torch.bfloat16 if self.device.type != "cpu" else torch.float32
         self.model = None
         self.processor = None
         self.is_loaded = False
-        self.token_cache = {}
-        self.load()  # Load persistently at initialization
         logger.info(f"LLMManager initialized with model {model_name} on {self.device}")
 
     def load(self):
         if not self.is_loaded:
             try:
-                if self.device.type == "cuda":
-                    torch.set_float32_matmul_precision('high')
-                    logger.info("Enabled TF32 matrix multiplication for improved GPU performance")
-
                 self.model = Gemma3ForConditionalGeneration.from_pretrained(
                     self.model_name,
                     device_map="auto",
-                    torch_dtype=torch.float16,  # Use float16 for speed
-                    max_memory={0: "10GiB"}
-                ).eval()
-
-                self.processor = AutoProcessor.from_pretrained(self.model_name, use_fast=True)
-                # Warm-up model
-                dummy_input = self.processor("test", return_tensors="pt").to(self.device)
-                with torch.no_grad():
-                    self.model.generate(**dummy_input, max_new_tokens=10)
+                    quantization_config=quantization_config,
+                    torch_dtype=self.torch_dtype
+                )
+                self.model.eval()
+                self.processor = AutoProcessor.from_pretrained(self.model_name)
                 self.is_loaded = True
-                logger.info(f"LLM {self.model_name} loaded and warmed up on {self.device}")
+                logger.info(f"LLM {self.model_name} loaded on {self.device}")
             except Exception as e:
                 logger.error(f"Failed to load LLM: {str(e)}")
-                self.is_loaded = False  # Allow graceful degradation
+                raise
 
     def unload(self):
         if self.is_loaded:
@@ -115,29 +111,74 @@ class LLMManager:
             del self.processor
             if self.device.type == "cuda":
                 torch.cuda.empty_cache()
-                logger.info(f"GPU memory cleared: {torch.cuda.memory_allocated()} bytes allocated")
+                logger.info(f"GPU memory allocated after unload: {torch.cuda.memory_allocated()}")
             self.is_loaded = False
-            self.token_cache.clear()
-            logger.info(f"LLM {self.model_name} unloaded")
+            logger.info(f"LLM {self.model_name} unloaded from {self.device}")
 
-    async def generate(self, prompt: str, max_tokens: int = settings.max_tokens, temperature: float = 0.7) -> str:
+    async def generate(self, prompt: str, max_tokens: int = 512, temperature: float = 0.7) -> str:
         if not self.is_loaded:
-            logger.warning("LLM not loaded; attempting reload")
             self.load()
-        if not self.is_loaded:
-            raise HTTPException(status_code=503, detail="LLM model unavailable")
 
-        # Improved cache key with parameters
-        cache_key = f"{prompt}:{max_tokens}:{temperature}"
-        if cache_key in self.token_cache:
-            logger.info("Using cached response")
-            return self.token_cache[cache_key]
+        messages_vlm = [
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Answer questions considering India as base country and Karnataka as base state. Provide a concise response in one sentence maximum."}]
+            },
+            {
+                "role": "user",
+                "content": [{"type": "text", "text": prompt}]
+            }
+        ]
+
+        try:
+            inputs_vlm = self.processor.apply_chat_template(
+                messages_vlm,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt"
+            ).to(self.device, dtype=torch.bfloat16)
+        except Exception as e:
+            logger.error(f"Error in tokenization: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Tokenization failed: {str(e)}")
+
+        input_len = inputs_vlm["input_ids"].shape[-1]
+
+        with torch.inference_mode():
+            generation = self.model.generate(
+                **inputs_vlm,
+                max_new_tokens=max_tokens,
+                do_sample=True,
+                temperature=temperature
+            )
+            generation = generation[0][input_len:]
+
+        response = self.processor.decode(generation, skip_special_tokens=True)
+        logger.info(f"Generated response: {response}")
+        return response
+
+    async def vision_query(self, image: Image.Image, query: str) -> str:
+        if not self.is_loaded:
+            self.load()
 
         messages_vlm = [
-            {"role": "system", "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Answer questions considering India as base country and Karnataka as base state. Provide a concise response in one sentence maximum."}]},
-            {"role": "user", "content": [{"type": "text", "text": prompt}]}
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Summarize your answer in maximum 1 sentence."}]
+            },
+            {
+                "role": "user",
+                "content": []
+            }
         ]
 
+        messages_vlm[1]["content"].append({"type": "text", "text": query})
+        if image and image.size[0] > 0 and image.size[1] > 0:
+            messages_vlm[1]["content"].insert(0, {"type": "image", "image": image})
+            logger.info(f"Received valid image for processing")
+        else:
+            logger.info("No valid image provided, processing text only")
+
         try:
             inputs_vlm = self.processor.apply_chat_template(
                 messages_vlm,
@@ -145,160 +186,248 @@ class LLMManager:
                 tokenize=True,
                 return_dict=True,
                 return_tensors="pt"
-            ).to(self.device)
-
-            with autocast():  # Mixed precision for speed
-                generation = self.model.generate(
-                    **inputs_vlm,
-                    max_new_tokens=max_tokens,
-                    do_sample=True,
-                    top_p=0.9,
-                    temperature=temperature
-                )
-                generation = generation[0][inputs_vlm["input_ids"].shape[-1]:]
+            ).to(self.device, dtype=torch.bfloat16)
+        except Exception as e:
+            logger.error(f"Error in apply_chat_template: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Failed to process input: {str(e)}")
+
+        input_len = inputs_vlm["input_ids"].shape[-1]
+
+        with torch.inference_mode():
+            generation = self.model.generate(
+                **inputs_vlm,
+                max_new_tokens=512,
+                do_sample=True,
+                temperature=0.7
+            )
+            generation = generation[0][input_len:]
+
+        decoded = self.processor.decode(generation, skip_special_tokens=True)
+        logger.info(f"Vision query response: {decoded}")
+        return decoded
+
+    async def chat_v2(self, image: Image.Image, query: str) -> str:
+        if not self.is_loaded:
+            self.load()
+
+        messages_vlm = [
+            {
+                "role": "system",
+                "content": [{"type": "text", "text": "You are Dhwani, a helpful assistant. Answer questions considering India as base country and Karnataka as base state."}]
+            },
+            {
+                "role": "user",
+                "content": []
+            }
+        ]
+
+        messages_vlm[1]["content"].append({"type": "text", "text": query})
+        if image and image.size[0] > 0 and image.size[1] > 0:
+            messages_vlm[1]["content"].insert(0, {"type": "image", "image": image})
+            logger.info(f"Received valid image for processing")
+        else:
+            logger.info("No valid image provided, processing text only")
 
-            response = self.processor.decode(generation, skip_special_tokens=True)
-            self.token_cache[cache_key] = response
-            logger.info(f"Generated response: {response}")
-            return response
+        try:
+            inputs_vlm = self.processor.apply_chat_template(
+                messages_vlm,
+                add_generation_prompt=True,
+                tokenize=True,
+                return_dict=True,
+                return_tensors="pt"
+            ).to(self.device, dtype=torch.bfloat16)
         except Exception as e:
-            logger.error(f"Error in generation: {str(e)}")
-            raise HTTPException(status_code=500, detail=f"Generation failed: {str(e)}")
+            logger.error(f"Error in apply_chat_template: {str(e)}")
+            raise HTTPException(status_code=500, detail=f"Failed to process input: {str(e)}")
+
+        input_len = inputs_vlm["input_ids"].shape[-1]
+
+        with torch.inference_mode():
+            generation = self.model.generate(
+                **inputs_vlm,
+                max_new_tokens=512,
+                do_sample=True,
+                temperature=0.7
+            )
+            generation = generation[0][input_len:]
+
+        decoded = self.processor.decode(generation, skip_special_tokens=True)
+        logger.info(f"Chat_v2 response: {decoded}")
+        return decoded
 
-# TTS Manager with file-based synthesis
+# TTS Manager
 class TTSManager:
     def __init__(self, device_type=device):
-        self.device_type = torch.device(device_type)
+        self.device_type = device_type
         self.model = None
         self.repo_id = "ai4bharat/IndicF5"
-        self.load()  # Persistent loading
 
     def load(self):
         if not self.model:
-            logger.info(f"Loading TTS model {self.repo_id} on {self.device_type}...")
-            self.model = AutoModel.from_pretrained(self.repo_id, trust_remote_code=True).to(self.device_type)
-            logger.info("TTS model loaded")
-
-    def unload(self):
-        if self.model:
-            del self.model
-            if self.device_type.type == "cuda":
-                torch.cuda.empty_cache()
-                logger.info(f"TTS GPU memory cleared: {torch.cuda.memory_allocated()} bytes allocated")
-            self.model = None
-            logger.info("TTS model unloaded")
+            logger.info("Loading TTS model IndicF5...")
+            self.model = AutoModel.from_pretrained(
+                self.repo_id,
+                trust_remote_code=True
+            )
+            self.model = self.model.to(self.device_type)
+            logger.info("TTS model IndicF5 loaded")
 
     def synthesize(self, text, ref_audio_path, ref_text):
         if not self.model:
             raise ValueError("TTS model not loaded")
-        with autocast():  # Mixed precision
-            return self.model(text, ref_audio_path=ref_audio_path, ref_text=ref_text)
+        return self.model(text, ref_audio_path=ref_audio_path, ref_text=ref_text)
+
+# TTS Constants
+EXAMPLES = [
+    {
+        "audio_name": "KAN_F (Happy)",
+        "audio_url": "https://github.com/AI4Bharat/IndicF5/raw/refs/heads/main/prompts/KAN_F_HAPPY_00001.wav",
+        "ref_text": "ನಮ್‌ ಫ್ರಿಜ್ಜಲ್ಲಿ  ಕೂಲಿಂಗ್‌ ಸಮಸ್ಯೆ ಆಗಿ ನಾನ್‌ ಭಾಳ ದಿನದಿಂದ ಒದ್��ಾಡ್ತಿದ್ದೆ, ಆದ್ರೆ ಅದ್ನೀಗ ಮೆಕಾನಿಕ್ ಆಗಿರೋ ನಿಮ್‌ ಸಹಾಯ್ದಿಂದ ಬಗೆಹರಿಸ್ಕೋಬೋದು ಅಂತಾಗಿ ನಿರಾಳ ಆಯ್ತು ನಂಗೆ.",
+        "synth_text": "ಚೆನ್ನೈನ ಶೇರ್ ಆಟೋ ಪ್ರಯಾಣಿಕರ ನಡುವೆ ಆಹಾರವನ್ನು ಹಂಚಿಕೊಂಡು ತಿನ್ನುವುದು ನನಗೆ ಮನಸ್ಸಿಗೆ ತುಂಬಾ ಒಳ್ಳೆಯದೆನಿಸುವ ವಿಷಯ."
+    },
+]
+
+# Pydantic models for TTS
+class SynthesizeRequest(BaseModel):
+    text: str
+    ref_audio_name: str
+    ref_text: str = None
 
-# Translation Manager with warm-up and error handling
+class KannadaSynthesizeRequest(BaseModel):
+    text: str
+
+# TTS Functions
+def load_audio_from_url(url: str):
+    response = requests.get(url)
+    if response.status_code == 200:
+        audio_data, sample_rate = sf.read(io.BytesIO(response.content))
+        return sample_rate, audio_data
+    raise HTTPException(status_code=500, detail="Failed to load reference audio from URL.")
+
+def synthesize_speech(tts_manager: TTSManager, text: str, ref_audio_name: str, ref_text: str):
+    ref_audio_url = None
+    for example in EXAMPLES:
+        if example["audio_name"] == ref_audio_name:
+            ref_audio_url = example["audio_url"]
+            if not ref_text:
+                ref_text = example["ref_text"]
+            break
+    
+    if not ref_audio_url:
+        raise HTTPException(status_code=400, detail="Invalid reference audio name.")
+    if not text.strip():
+        raise HTTPException(status_code=400, detail="Text to synthesize cannot be empty.")
+    if not ref_text or not ref_text.strip():
+        raise HTTPException(status_code=400, detail="Reference text cannot be empty.")
+
+    sample_rate, audio_data = load_audio_from_url(ref_audio_url)
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_audio:
+        sf.write(temp_audio.name, audio_data, samplerate=sample_rate, format='WAV')
+        temp_audio.flush()
+        audio = tts_manager.synthesize(text, ref_audio_path=temp_audio.name, ref_text=ref_text)
+
+    if audio.dtype == np.int16:
+        audio = audio.astype(np.float32) / 32768.0
+    buffer = io.BytesIO()
+    sf.write(buffer, audio, 24000, format='WAV')
+    buffer.seek(0)
+    return buffer
+
+# Supported languages
+SUPPORTED_LANGUAGES = {
+    "asm_Beng", "kas_Arab", "pan_Guru", "ben_Beng", "kas_Deva", "san_Deva",
+    "brx_Deva", "mai_Deva", "sat_Olck", "doi_Deva", "mal_Mlym", "snd_Arab",
+    "eng_Latn", "mar_Deva", "snd_Deva", "gom_Deva", "mni_Beng", "tam_Taml",
+    "guj_Gujr", "mni_Mtei", "tel_Telu", "hin_Deva", "npi_Deva", "urd_Arab",
+    "kan_Knda", "ory_Orya",
+    "deu_Latn", "fra_Latn", "nld_Latn", "spa_Latn", "ita_Latn",
+    "por_Latn", "rus_Cyrl", "pol_Latn"
+}
+
+# Translation Manager
 class TranslateManager:
     def __init__(self, src_lang, tgt_lang, device_type=device, use_distilled=True):
-        self.device_type = torch.device(device_type)
-        self.tokenizer, self.model = self.initialize_model(src_lang, tgt_lang, use_distilled)
-        if self.model:
-            self.warm_up()
+        self.device_type = device_type
+        self.tokenizer = None
+        self.model = None
+        self.src_lang = src_lang
+        self.tgt_lang = tgt_lang
+        self.use_distilled = use_distilled
 
-    def initialize_model(self, src_lang, tgt_lang, use_distilled=True):
-        try:
-            if src_lang.startswith("eng") and not tgt_lang.startswith("eng"):
-                model_name = "ai4bharat/indictrans2-en-indic-dist-200M" if use_distilled else "ai4bharat/indictrans2-en-indic-1B"
-            elif not src_lang.startswith("eng") and tgt_lang.startswith("eng"):
-                model_name = "ai4bharat/indictrans2-indic-en-dist-200M" if use_distilled else "ai4bharat/indictrans2-indic-en-1B"
-            elif not src_lang.startswith("eng") and not tgt_lang.startswith("eng"):
-                model_name = "ai4bharat/indictrans2-indic-indic-dist-320M" if use_distilled else "ai4bharat/indictrans2-indic-indic-1B"
+    def load(self):
+        if not self.tokenizer or not self.model:
+            if self.src_lang.startswith("eng") and not self.tgt_lang.startswith("eng"):
+                model_name = "ai4bharat/indictrans2-en-indic-dist-200M" if self.use_distilled else "ai4bharat/indictrans2-en-indic-1B"
+            elif not self.src_lang.startswith("eng") and self.tgt_lang.startswith("eng"):
+                model_name = "ai4bharat/indictrans2-indic-en-dist-200M" if self.use_distilled else "ai4bharat/indictrans2-indic-en-1B"
+            elif not self.src_lang.startswith("eng") and not self.tgt_lang.startswith("eng"):
+                model_name = "ai4bharat/indictrans2-indic-indic-dist-320M" if self.use_distilled else "ai4bharat/indictrans2-indic-indic-1B"
             else:
                 raise ValueError("Invalid language combination")
 
-            tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-            model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.tokenizer = AutoTokenizer.from_pretrained(
+                model_name,
+                trust_remote_code=True
+            )
+            self.model = AutoModelForSeq2SeqLM.from_pretrained(
                 model_name,
                 trust_remote_code=True,
                 torch_dtype=torch.float16,
                 attn_implementation="flash_attention_2"
-            ).to(self.device_type)
-            return tokenizer, model
-        except Exception as e:
-            logger.error(f"Failed to load translation model: {str(e)}")
-            return None, None  # Graceful degradation
-
-    def warm_up(self):
-        dummy_input = self.tokenizer("test", return_tensors="pt").to(self.device_type)
-        with torch.no_grad(), autocast():
-            self.model.generate(**dummy_input, max_length=10)
-        logger.info("Translation model warmed up")
-
-    def unload(self):
-        if self.model:
-            del self.model
-            del self.tokenizer
-            if self.device_type.type == "cuda":
-                torch.cuda.empty_cache()
-                logger.info(f"Translation GPU memory cleared: {torch.cuda.memory_allocated()} bytes allocated")
-            self.model = None
-            self.tokenizer = None
-            logger.info("Translation model unloaded")
+            )
+            self.model = self.model.to(self.device_type)
+            self.model = torch.compile(self.model, mode="reduce-overhead")
+            logger.info(f"Translation model {model_name} loaded")
 
-# Model Manager with preloading
 class ModelManager:
-    def __init__(self, device_type=device, use_distilled=True):
+    def __init__(self, device_type=device, use_distilled=True, is_lazy_loading=False):
         self.models = {}
         self.device_type = device_type
         self.use_distilled = use_distilled
-        self.preload_models()
+        self.is_lazy_loading = is_lazy_loading
 
-    def preload_models(self):
-        translation_pairs = [
-            ('eng_Latn', 'kan_Knda', 'eng_indic'),
-            ('kan_Knda', 'eng_Latn', 'indic_eng'),
-            ('kan_Knda', 'hin_Deva', 'indic_indic')
-        ]
-        for src_lang, tgt_lang, key in translation_pairs:
-            logger.info(f"Preloading translation model for {src_lang} -> {tgt_lang}...")
-            self.models[key] = TranslateManager(src_lang, tgt_lang, self.device_type, self.use_distilled)
+    def load_model(self, src_lang, tgt_lang, key):
+        logger.info(f"Loading translation model for {src_lang} -> {tgt_lang}")
+        translate_manager = TranslateManager(src_lang, tgt_lang, self.device_type, self.use_distilled)
+        translate_manager.load()
+        self.models[key] = translate_manager
+        logger.info(f"Loaded translation model for {key}")
 
     def get_model(self, src_lang, tgt_lang):
+        key = self._get_model_key(src_lang, tgt_lang)
+        if key not in self.models:
+            if self.is_lazy_loading:
+                self.load_model(src_lang, tgt_lang, key)
+            else:
+                raise ValueError(f"Model for {key} is not preloaded and lazy loading is disabled.")
+        return self.models.get(key)
+
+    def _get_model_key(self, src_lang, tgt_lang):
         if src_lang.startswith("eng") and not tgt_lang.startswith("eng"):
-            key = 'eng_indic'
+            return 'eng_indic'
         elif not src_lang.startswith("eng") and tgt_lang.startswith("eng"):
-            key = 'indic_eng'
+            return 'indic_eng'
         elif not src_lang.startswith("eng") and not tgt_lang.startswith("eng"):
-            key = 'indic_indic'
-        else:
-            raise ValueError("Invalid language combination")
-        if key not in self.models or not self.models[key].model:
-            raise HTTPException(status_code=503, detail=f"Translation model for {key} unavailable")
-        return self.models[key]
+            return 'indic_indic'
+        raise ValueError("Invalid language combination")
 
-# ASR Manager with GPU audio processing
+# ASR Manager
 class ASRModelManager:
-    def __init__(self, device_type=device):
-        self.device_type = torch.device(device_type)
+    def __init__(self, device_type="cuda"):
+        self.device_type = device_type
         self.model = None
         self.model_language = {"kannada": "kn"}
-        self.load()
 
     def load(self):
         if not self.model:
-            logger.info(f"Loading ASR model on {self.device_type}...")
+            logger.info("Loading ASR model...")
             self.model = AutoModel.from_pretrained(
                 "ai4bharat/indic-conformer-600m-multilingual",
                 trust_remote_code=True
-            ).to(self.device_type)
+            )
+            self.model = self.model.to(self.device_type)
             logger.info("ASR model loaded")
 
-    def unload(self):
-        if self.model:
-            del self.model
-            if self.device_type.type == "cuda":
-                torch.cuda.empty_cache()
-                logger.info(f"ASR GPU memory cleared: {torch.cuda.memory_allocated()} bytes allocated")
-            self.model = None
-            logger.info("ASR model unloaded")
-
 # Global Managers
 llm_manager = LLMManager(settings.llm_model_name)
 model_manager = ModelManager()
@@ -306,15 +435,6 @@ asr_manager = ASRModelManager()
 tts_manager = TTSManager()
 ip = IndicProcessor(inference=True)
 
-# TTS Constants
-EXAMPLES = [
-    {
-        "audio_name": "KAN_F (Happy)",
-        "audio_url": "https://github.com/AI4Bharat/IndicF5/raw/refs/heads/main/prompts/KAN_F_HAPPY_00001.wav",
-        "ref_text": "ನಮ್‌ ಫ್ರಿಜ್ಜಲ್ಲಿ ಕೂಲಿಂಗ್‌ ಸಮಸ್ಯೆ ಆಗಿ ನಾನ್‌ ಭಾಳ ದಿನದಿಂದ ಒದ್ದಾಡ್ತಿದ್ದೆ, ಆದ್ರೆ ಅದ್ನೀಗ ಮೆಕಾನಿಕ್ ಆಗಿರೋ ನಿಮ್‌ ಸಹಾಯ್ದಿಂದ ಬಗೆಹರಿಸ್ಕೋಬೋದು ಅಂತಾಗಿ ನಿರಾಳ ಆಯ್ತು ನಂಗೆ।",
-    },
-]
-
 # Pydantic Models
 class ChatRequest(BaseModel):
     prompt: str
@@ -327,70 +447,92 @@ class ChatRequest(BaseModel):
             raise ValueError("Prompt cannot exceed 1000 characters")
         return v.strip()
 
+    @field_validator("src_lang", "tgt_lang")
+    def validate_language(cls, v):
+        if v not in SUPPORTED_LANGUAGES:
+            raise ValueError(f"Unsupported language code: {v}. Supported codes: {', '.join(SUPPORTED_LANGUAGES)}")
+        return v
+
+
 class ChatResponse(BaseModel):
     response: str
 
-class KannadaSynthesizeRequest(BaseModel):
-    text: str
-
-    @field_validator("text")
-    def text_must_be_valid(cls, v):
-        if len(v) > 500:
-            raise ValueError("Text cannot exceed 500 characters")
-        return v.strip()
+class TranslationRequest(BaseModel):
+    sentences: List[str]
+    src_lang: str
+    tgt_lang: str
 
 class TranscriptionResponse(BaseModel):
     text: str
 
-# TTS Functions
-@retry(stop=stop_after_attempt(3), wait=wait_exponential(min=1, max=10))
-def load_audio_from_url(url: str):
-    response = requests.get(url)
-    if response.status_code == 200:
-        audio_data, sample_rate = sf.read(io.BytesIO(response.content))
-        return sample_rate, audio_data
-    raise HTTPException(status_code=500, detail="Failed to load reference audio from URL after retries")
-
-async def synthesize_speech(tts_manager: TTSManager, text: str, ref_audio_name: str, ref_text: str) -> io.BytesIO:
-    async with request_queue:
-        ref_audio_url = None
-        for example in EXAMPLES:
-            if example["audio_name"] == ref_audio_name:
-                ref_audio_url = example["audio_url"]
-                if not ref_text:
-                    ref_text = example["ref_text"]
-                break
-
-        if not ref_audio_url:
-            raise HTTPException(status_code=400, detail=f"Invalid reference audio name: {ref_audio_name}")
-        if not text.strip() or not ref_text.strip():
-            raise HTTPException(status_code=400, detail="Text or reference text cannot be empty")
-
-        logger.info(f"Synthesizing speech for text: {text[:50]}... with ref_audio: {ref_audio_name}")
-        sample_rate, audio_data = load_audio_from_url(ref_audio_url)
-
-        # Use temporary file since IndicF5 requires a path
-        with tempfile.NamedTemporaryFile(suffix=".wav", delete=True) as temp_ref_audio:
-            sf.write(temp_ref_audio.name, audio_data, sample_rate, format='WAV')
-            temp_ref_audio.flush()
-            audio = tts_manager.synthesize(text, temp_ref_audio.name, ref_text)
-
-        if audio.dtype == np.int16:
-            audio = audio.astype(np.float32) / 32768.0
-        output_buffer = io.BytesIO()
-        sf.write(output_buffer, audio, 24000, format='WAV')
-        output_buffer.seek(0)
-        logger.info("Speech synthesis completed")
-        return output_buffer
+class TranslationResponse(BaseModel):
+    translations: List[str]
+
+# Dependency
+def get_translate_manager(src_lang: str, tgt_lang: str) -> TranslateManager:
+    return model_manager.get_model(src_lang, tgt_lang)
+
+# Lifespan Event Handler
+translation_configs = []
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    def load_all_models():
+        try:
+            # Load LLM model
+            logger.info("Loading LLM model...")
+            llm_manager.load()
+            logger.info("LLM model loaded successfully")
+
+            # Load TTS model
+            logger.info("Loading TTS model...")
+            tts_manager.load()
+            logger.info("TTS model loaded successfully")
+
+            # Load ASR model
+            logger.info("Loading ASR model...")
+            asr_manager.load()
+            logger.info("ASR model loaded successfully")
+
+            # Load translation models
+            translation_tasks = [
+                ('eng_Latn', 'kan_Knda', 'eng_indic'),
+                ('kan_Knda', 'eng_Latn', 'indic_eng'),
+                ('kan_Knda', 'hin_Deva', 'indic_indic'),
+            ]
+            
+            for config in translation_configs:
+                src_lang = config["src_lang"]
+                tgt_lang = config["tgt_lang"]
+                key = model_manager._get_model_key(src_lang, tgt_lang)
+                translation_tasks.append((src_lang, tgt_lang, key))
+
+            for src_lang, tgt_lang, key in translation_tasks:
+                logger.info(f"Loading translation model for {src_lang} -> {tgt_lang}...")
+                model_manager.load_model(src_lang, tgt_lang, key)
+                logger.info(f"Translation model for {key} loaded successfully")
+
+            logger.info("All models loaded successfully")
+        except Exception as e:
+            logger.error(f"Error loading models: {str(e)}")
+            raise
+
+    logger.info("Starting sequential model loading...")
+    load_all_models()
+    yield
+    llm_manager.unload()
+    logger.info("Server shutdown complete")
 
 # FastAPI App
 app = FastAPI(
-    title="Optimized Dhwani API",
-    description="AI Chat API with optimized performance and robustness",
+    title="Dhwani API",
+    description="AI Chat API supporting Indian languages",
     version="1.0.0",
+    redirect_slashes=False,
     lifespan=lifespan
 )
 
+# Add CORS Middleware
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -399,6 +541,7 @@ app.add_middleware(
     allow_headers=["*"],
 )
 
+# Add Timing Middleware
 @app.middleware("http")
 async def add_request_timing(request: Request, call_next):
     start_time = time()
@@ -412,157 +555,375 @@ async def add_request_timing(request: Request, call_next):
 limiter = Limiter(key_func=get_remote_address)
 app.state.limiter = limiter
 
-# Lifespan Event Handler
-@asynccontextmanager
-async def lifespan(app: FastAPI):
-    logger.info("Starting server with preloaded models...")
-    yield
-    llm_manager.unload()
-    tts_manager.unload()
-    asr_manager.unload()
-    for model in model_manager.models.values():
-        model.unload()
-    logger.info("Server shutdown complete; all models unloaded")
-
-# Endpoints
-@app.post("/v1/speech_to_speech", response_class=StreamingResponse)
-async def speech_to_speech(
-    request: Request,
+# API Endpoints
+@app.post("/audio/speech", response_class=StreamingResponse)
+async def synthesize_kannada(request: KannadaSynthesizeRequest):
+    if not tts_manager.model:
+        raise HTTPException(status_code=503, detail="TTS model not loaded")
+    kannada_example = next(ex for ex in EXAMPLES if ex["audio_name"] == "KAN_F (Happy)")
+    if not request.text.strip():
+        raise HTTPException(status_code=400, detail="Text to synthesize cannot be empty.")
+    
+    audio_buffer = synthesize_speech(
+        tts_manager,
+        text=request.text,
+        ref_audio_name="KAN_F (Happy)",
+        ref_text=kannada_example["ref_text"]
+    )
+    
+    return StreamingResponse(
+        audio_buffer,
+        media_type="audio/wav",
+        headers={"Content-Disposition": "attachment; filename=synthesized_kannada_speech.wav"}
+    )
+
+@app.post("/translate", response_model=TranslationResponse)
+async def translate(request: TranslationRequest, translate_manager: TranslateManager = Depends(get_translate_manager)):
+    input_sentences = request.sentences
+    src_lang = request.src_lang
+    tgt_lang = request.tgt_lang
+
+    if not input_sentences:
+        raise HTTPException(status_code=400, detail="Input sentences are required")
+
+    batch = ip.preprocess_batch(input_sentences, src_lang=src_lang, tgt_lang=tgt_lang)
+    inputs = translate_manager.tokenizer(
+        batch,
+        truncation=True,
+        padding="longest",
+        return_tensors="pt",
+        return_attention_mask=True,
+    ).to(translate_manager.device_type)
+
+    with torch.no_grad():
+        generated_tokens = translate_manager.model.generate(
+            **inputs,
+            use_cache=True,
+            min_length=0,
+            max_length=256,
+            num_beams=5,
+            num_return_sequences=1,
+        )
+
+    with translate_manager.tokenizer.as_target_tokenizer():
+        generated_tokens = translate_manager.tokenizer.batch_decode(
+            generated_tokens.detach().cpu().tolist(),
+            skip_special_tokens=True,
+            clean_up_tokenization_spaces=True,
+        )
+
+    translations = ip.postprocess_batch(generated_tokens, lang=tgt_lang)
+    return TranslationResponse(translations=translations)
+
+async def perform_internal_translation(sentences: List[str], src_lang: str, tgt_lang: str) -> List[str]:
+    try:
+        translate_manager = model_manager.get_model(src_lang, tgt_lang)
+    except ValueError as e:
+        logger.info(f"Model not preloaded: {str(e)}, loading now...")
+        key = model_manager._get_model_key(src_lang, tgt_lang)
+        model_manager.load_model(src_lang, tgt_lang, key)
+        translate_manager = model_manager.get_model(src_lang, tgt_lang)
+    
+    if not translate_manager.model:
+        translate_manager.load()
+    
+    request = TranslationRequest(sentences=sentences, src_lang=src_lang, tgt_lang=tgt_lang)
+    response = await translate(request, translate_manager)
+    return response.translations
+
+@app.get("/v1/health")
+async def health_check():
+    return {"status": "healthy", "model": settings.llm_model_name}
+
+@app.get("/")
+async def home():
+    return RedirectResponse(url="/docs")
+
+@app.post("/v1/unload_all_models")
+async def unload_all_models():
+    try:
+        logger.info("Starting to unload all models...")
+        llm_manager.unload()
+        logger.info("All models unloaded successfully")
+        return {"status": "success", "message": "All models unloaded"}
+    except Exception as e:
+        logger.error(f"Error unloading models: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to unload models: {str(e)}")
+
+@app.post("/v1/load_all_models")
+async def load_all_models():
+    try:
+        logger.info("Starting to load all models...")
+        llm_manager.load()
+        logger.info("All models loaded successfully")
+        return {"status": "success", "message": "All models loaded"}
+    except Exception as e:
+        logger.error(f"Error loading models: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Failed to load models: {str(e)}")
+
+@app.post("/v1/translate", response_model=TranslationResponse)
+async def translate_endpoint(request: TranslationRequest):
+    logger.info(f"Received translation request: {request.dict()}")
+    try:
+        translations = await perform_internal_translation(
+            sentences=request.sentences,
+            src_lang=request.src_lang,
+            tgt_lang=request.tgt_lang
+        )
+        logger.info(f"Translation successful: {translations}")
+        return TranslationResponse(translations=translations)
+    except Exception as e:
+        logger.error(f"Unexpected error during translation: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Translation failed: {str(e)}")
+
+@app.post("/v1/chat", response_model=ChatResponse)
+@limiter.limit(settings.chat_rate_limit)
+async def chat(request: Request, chat_request: ChatRequest):
+    if not chat_request.prompt:
+        raise HTTPException(status_code=400, detail="Prompt cannot be empty")
+    logger.info(f"Received prompt: {chat_request.prompt}, src_lang: {chat_request.src_lang}, tgt_lang: {chat_request.tgt_lang}")
+    
+    EUROPEAN_LANGUAGES = {"deu_Latn", "fra_Latn", "nld_Latn", "spa_Latn", "ita_Latn", "por_Latn", "rus_Cyrl", "pol_Latn"}
+    
+    try:
+        if chat_request.src_lang != "eng_Latn" and chat_request.src_lang not in EUROPEAN_LANGUAGES:
+            translated_prompt = await perform_internal_translation(
+                sentences=[chat_request.prompt],
+                src_lang=chat_request.src_lang,
+                tgt_lang="eng_Latn"
+            )
+            prompt_to_process = translated_prompt[0]
+            logger.info(f"Translated prompt to English: {prompt_to_process}")
+        else:
+            prompt_to_process = chat_request.prompt
+            logger.info("Prompt in English or European language, no translation needed")
+
+        response = await llm_manager.generate(prompt_to_process, settings.max_tokens)
+        logger.info(f"Generated response: {response}")
+
+        if chat_request.tgt_lang != "eng_Latn" and chat_request.tgt_lang not in EUROPEAN_LANGUAGES:
+            translated_response = await perform_internal_translation(
+                sentences=[response],
+                src_lang="eng_Latn",
+                tgt_lang=chat_request.tgt_lang
+            )
+            final_response = translated_response[0]
+            logger.info(f"Translated response to {chat_request.tgt_lang}: {final_response}")
+        else:
+            final_response = response
+            logger.info(f"Response in {chat_request.tgt_lang}, no translation needed")
+
+        return ChatResponse(response=final_response)
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+@app.post("/v1/visual_query/")
+async def visual_query(
     file: UploadFile = File(...),
-    language: str = Query(..., enum=list(asr_manager.model_language.keys())),
+    query: str = Body(...),
+    src_lang: str = Query("kan_Knda", enum=list(SUPPORTED_LANGUAGES)),
+    tgt_lang: str = Query("kan_Knda", enum=list(SUPPORTED_LANGUAGES)),
 ):
-    async with request_queue:
-        if not tts_manager.model or not asr_manager.model:
-            raise HTTPException(status_code=503, detail="TTS or ASR model not loaded")
+    try:
+        image = Image.open(file.file)
+        if image.size == (0, 0):
+            raise HTTPException(status_code=400, detail="Uploaded image is empty or invalid")
+
+        if src_lang != "eng_Latn":
+            translated_query = await perform_internal_translation(
+                sentences=[query],
+                src_lang=src_lang,
+                tgt_lang="eng_Latn"
+            )
+            query_to_process = translated_query[0]
+            logger.info(f"Translated query to English: {query_to_process}")
+        else:
+            query_to_process = query
+            logger.info("Query already in English, no translation needed")
 
-        audio_data = await file.read()
-        if not audio_data:
-            raise HTTPException(status_code=400, detail="Uploaded audio file is empty")
-        if len(audio_data) > 10 * 1024 * 1024:
-            raise HTTPException(status_code=400, detail="Audio file exceeds 10MB limit")
+        answer = await llm_manager.vision_query(image, query_to_process)
+        logger.info(f"Generated English answer: {answer}")
 
-        logger.info(f"Processing speech-to-speech for file: {file.filename} in language: {language}")
-        try:
-            # GPU-accelerated transcription
-            wav, sr = torchaudio.load(io.BytesIO(audio_data), backend="cuda" if cuda_available else "cpu")
-            wav = torch.mean(wav, dim=0, keepdim=True).to(device)
-            target_sample_rate = 16000
-            if sr != target_sample_rate:
-                resampler = torchaudio.transforms.Resample(sr, target_sample_rate).to(device)
-                wav = resampler(wav)
-            with autocast(), torch.no_grad():
-                transcription = asr_manager.model(wav, asr_manager.model_language[language], "rnnt")
-            logger.info(f"Transcribed text: {transcription[:50]}...")
-
-            chat_request = ChatRequest(
-                prompt=transcription,
-                src_lang="kan_Knda",
-                tgt_lang="kan_Knda"
+        if tgt_lang != "eng_Latn":
+            translated_answer = await perform_internal_translation(
+                sentences=[answer],
+                src_lang="eng_Latn",
+                tgt_lang=tgt_lang
             )
-            translate_mgr = model_manager.get_model(chat_request.src_lang, "eng_Latn")
-            if translate_mgr.model:
+            final_answer = translated_answer[0]
+            logger.info(f"Translated answer to {tgt_lang}: {final_answer}")
+        else:
+            final_answer = answer
+            logger.info("Answer kept in English, no translation needed")
+
+        return {"answer": final_answer}
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+@app.post("/v1/chat_v2", response_model=ChatResponse)
+@limiter.limit(settings.chat_rate_limit)
+async def chat_v2(
+    request: Request,
+    prompt: str = Form(...),
+    image: UploadFile = File(default=None),
+    src_lang: str = Form("kan_Knda"),
+    tgt_lang: str = Form("kan_Knda"),
+):
+    if not prompt:
+        raise HTTPException(status_code=400, detail="Prompt cannot be empty")
+    if src_lang not in SUPPORTED_LANGUAGES or tgt_lang not in SUPPORTED_LANGUAGES:
+        raise HTTPException(status_code=400, detail=f"Unsupported language code. Supported codes: {', '.join(SUPPORTED_LANGUAGES)}")
+
+    logger.info(f"Received prompt: {prompt}, src_lang: {src_lang}, tgt_lang: {tgt_lang}, Image provided: {image is not None}")
+
+    try:
+        if image:
+            image_data = await image.read()
+            if not image_data:
+                raise HTTPException(status_code=400, detail="Uploaded image is empty")
+            img = Image.open(io.BytesIO(image_data))
+
+            if src_lang != "eng_Latn":
                 translated_prompt = await perform_internal_translation(
-                    [chat_request.prompt], chat_request.src_lang, "eng_Latn"
+                    sentences=[prompt],
+                    src_lang=src_lang,
+                    tgt_lang="eng_Latn"
                 )
                 prompt_to_process = translated_prompt[0]
+                logger.info(f"Translated prompt to English: {prompt_to_process}")
             else:
-                prompt_to_process = chat_request.prompt
-
-            response = await llm_manager.generate(prompt_to_process)
-            if chat_request.tgt_lang != "eng_Latn":
-                translate_mgr = model_manager.get_model("eng_Latn", chat_request.tgt_lang)
-                if translate_mgr.model:
-                    translated_response = await perform_internal_translation(
-                        [response], "eng_Latn", chat_request.tgt_lang
-                    )
-                    final_response = translated_response[0]
-                else:
-                    final_response = response
-            else:
-                final_response = response
-            logger.info(f"Processed text: {final_response[:50]}...")
-
-            audio_buffer = await synthesize_speech(tts_manager, final_response, "KAN_F (Happy)", EXAMPLES[0]["ref_text"])
-            logger.info("Speech-to-speech processing completed")
-            return StreamingResponse(
-                audio_buffer,
-                media_type="audio/wav",
-                headers={"Content-Disposition": "attachment; filename=speech_to_speech_output.wav"}
-            )
-        except Exception as e:
-            logger.error(f"Error in speech-to-speech pipeline: {str(e)}")
-            raise HTTPException(status_code=500, detail=f"Speech-to-speech failed: {str(e)}")
+                prompt_to_process = prompt
+                logger.info("Prompt already in English, no translation needed")
 
-@app.post("/v1/chat", response_model=ChatResponse)
-@limiter.limit(settings.chat_rate_limit)
-async def chat(request: Request, chat_request: ChatRequest):
-    async with request_queue:
-        logger.info(f"Received prompt: {chat_request.prompt}, src_lang: {chat_request.src_lang}, tgt_lang: {chat_request.tgt_lang}")
-        try:
-            if chat_request.src_lang != "eng_Latn":
-                translate_mgr = model_manager.get_model(chat_request.src_lang, "eng_Latn")
-                if translate_mgr.model:
-                    translated_prompt = await perform_internal_translation(
-                        [chat_request.prompt], chat_request.src_lang, "eng_Latn"
-                    )
-                    prompt_to_process = translated_prompt[0]
-                    logger.info(f"Translated prompt to English: {prompt_to_process}")
-                else:
-                    prompt_to_process = chat_request.prompt
+            decoded = await llm_manager.chat_v2(img, prompt_to_process)
+            logger.info(f"Generated English response: {decoded}")
+
+            if tgt_lang != "eng_Latn":
+                translated_response = await perform_internal_translation(
+                    sentences=[decoded],
+                    src_lang="eng_Latn",
+                    tgt_lang=tgt_lang
+                )
+                final_response = translated_response[0]
+                logger.info(f"Translated response to {tgt_lang}: {final_response}")
             else:
-                prompt_to_process = chat_request.prompt
-
-            response = await llm_manager.generate(prompt_to_process)
-            logger.info(f"Generated English response: {response}")
-
-            if chat_request.tgt_lang != "eng_Latn":
-                translate_mgr = model_manager.get_model("eng_Latn", chat_request.tgt_lang)
-                if translate_mgr.model:
-                    translated_response = await perform_internal_translation(
-                        [response], "eng_Latn", chat_request.tgt_lang
-                    )
-                    final_response = translated_response[0]
-                    logger.info(f"Translated response to {chat_request.tgt_lang}: {final_response}")
-                else:
-                    final_response = response
+                final_response = decoded
+                logger.info("Response kept in English, no translation needed")
+        else:
+            if src_lang != "eng_Latn":
+                translated_prompt = await perform_internal_translation(
+                    sentences=[prompt],
+                    src_lang=src_lang,
+                    tgt_lang="eng_Latn"
+                )
+                prompt_to_process = translated_prompt[0]
+                logger.info(f"Translated prompt to English: {prompt_to_process}")
             else:
-                final_response = response
-            return ChatResponse(response=final_response)
-        except Exception as e:
-            logger.error(f"Error in chat: {str(e)}")
-            raise HTTPException(status_code=500, detail=f"Chat failed: {str(e)}")
-
-async def perform_internal_translation(sentences: List[str], src_lang: str, tgt_lang: str) -> List[str]:
-    translate_mgr = model_manager.get_model(src_lang, tgt_lang)
-    if not translate_mgr.model:
-        raise HTTPException(status_code=503, detail="Translation model unavailable")
-    batch = ip.preprocess_batch(sentences, src_lang=src_lang, tgt_lang=tgt_lang)
-    inputs = translate_mgr.tokenizer(batch, truncation=True, padding="longest", return_tensors="pt").to(device)
-    with torch.no_grad(), autocast():
-        tokens = translate_mgr.model.generate(**inputs, max_length=256, num_beams=5)
-    translations = translate_mgr.tokenizer.batch_decode(tokens, skip_special_tokens=True)
-    return ip.postprocess_batch(translations, lang=tgt_lang)
+                prompt_to_process = prompt
+                logger.info("Prompt already in English, no translation needed")
 
-@app.get("/v1/health")
-async def health_check():
-    memory_usage = torch.cuda.memory_allocated() / (24 * 1024**3) if cuda_available else 0  # 24GB VRAM
-    if memory_usage > 0.9:
-        logger.warning("GPU memory usage exceeds 90%; consider unloading models")
-    status = {
-        "status": "healthy",
-        "llm_loaded": llm_manager.is_loaded,
-        "tts_loaded": bool(tts_manager.model),
-        "asr_loaded": bool(asr_manager.model),
-        "translation_models": list(model_manager.models.keys()),
-        "gpu_memory_usage": f"{memory_usage:.2%}"
-    }
-    return status
+            decoded = await llm_manager.generate(prompt_to_process, settings.max_tokens)
+            logger.info(f"Generated English response: {decoded}")
 
+            if tgt_lang != "eng_Latn":
+                translated_response = await perform_internal_translation(
+                    sentences=[decoded],
+                    src_lang="eng_Latn",
+                    tgt_lang=tgt_lang
+                )
+                final_response = translated_response[0]
+                logger.info(f"Translated response to {tgt_lang}: {final_response}")
+            else:
+                final_response = decoded
+                logger.info("Response kept in English, no translation needed")
+
+        return ChatResponse(response=final_response)
+    except Exception as e:
+        logger.error(f"Error processing request: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"An error occurred: {str(e)}")
+
+@app.post("/transcribe/", response_model=TranscriptionResponse)
+async def transcribe_audio(file: UploadFile = File(...), language: str = Query(..., enum=list(asr_manager.model_language.keys()))):
+    if not asr_manager.model:
+        raise HTTPException(status_code=503, detail="ASR model not loaded")
+    try:
+        wav, sr = torchaudio.load(file.file)
+        wav = torch.mean(wav, dim=0, keepdim=True)
+        target_sample_rate = 16000
+        if sr != target_sample_rate:
+            resampler = torchaudio.transforms.Resample(orig_freq=sr, new_freq=target_sample_rate)
+            wav = resampler(wav)
+        transcription_rnnt = asr_manager.model(wav, asr_manager.model_language[language], "rnnt")
+        return TranscriptionResponse(text=transcription_rnnt)
+    except Exception as e:
+        logger.error(f"Error in transcription: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")
+
+@app.post("/v1/speech_to_speech")
+async def speech_to_speech(
+    request: Request,
+    file: UploadFile = File(...),
+    language: str = Query(..., enum=list(asr_manager.model_language.keys())),
+) -> StreamingResponse:
+    if not tts_manager.model:
+        raise HTTPException(status_code=503, detail="TTS model not loaded")
+    transcription = await transcribe_audio(file, language)
+    logger.info(f"Transcribed text: {transcription.text}")
+
+    chat_request = ChatRequest(
+        prompt=transcription.text,
+        src_lang=LANGUAGE_TO_SCRIPT.get(language, "kan_Knda"),
+        tgt_lang=LANGUAGE_TO_SCRIPT.get(language, "kan_Knda")
+    )
+    processed_text = await chat(request, chat_request)
+    logger.info(f"Processed text: {processed_text.response}")
+
+    voice_request = KannadaSynthesizeRequest(text=processed_text.response)
+    audio_response = await synthesize_kannada(voice_request)
+    return audio_response
+
+LANGUAGE_TO_SCRIPT = {
+    "kannada": "kan_Knda"
+}
+
+# Main Execution
 if __name__ == "__main__":
     parser = argparse.ArgumentParser(description="Run the FastAPI server.")
     parser.add_argument("--port", type=int, default=settings.port, help="Port to run the server on.")
     parser.add_argument("--host", type=str, default=settings.host, help="Host to run the server on.")
+    parser.add_argument("--config", type=str, default="config_one", help="Configuration to use")
     args = parser.parse_args()
 
-    # Uvicorn tuning: 2 workers for 8 vCPUs and 24GB VRAM
-    uvicorn.run(app, host=args.host, port=args.port, workers=2)
+    def load_config(config_path="dhwani_config.json"):
+        with open(config_path, "r") as f:
+            return json.load(f)
+
+    config_data = load_config()
+    if args.config not in config_data["configs"]:
+        raise ValueError(f"Invalid config: {args.config}. Available: {list(config_data['configs'].keys())}")
+    
+    selected_config = config_data["configs"][args.config]
+    global_settings = config_data["global_settings"]
+
+    settings.llm_model_name = selected_config["components"]["LLM"]["model"]
+    settings.max_tokens = selected_config["components"]["LLM"]["max_tokens"]
+    settings.host = global_settings["host"]
+    settings.port = global_settings["port"]
+    settings.chat_rate_limit = global_settings["chat_rate_limit"]
+    settings.speech_rate_limit = global_settings["speech_rate_limit"]
+
+    llm_manager = LLMManager(settings.llm_model_name)
+
+    if selected_config["components"]["ASR"]:
+        asr_model_name = selected_config["components"]["ASR"]["model"]
+        asr_manager.model_language[selected_config["language"]] = selected_config["components"]["ASR"]["language_code"]
+
+    if selected_config["components"]["Translation"]:
+        translation_configs.extend(selected_config["components"]["Translation"])
+
+    host = args.host if args.host != settings.host else settings.host
+    port = args.port if args.port != settings.port else settings.port
+
+    uvicorn.run(app, host=host, port=port)