app.py

import gradio as gr
import torch
import os
import time

# --- Try to import ctransformers for GGUF, provide helpful message if not found ---
# We try to import ctransformers first as it's the preferred method for ZeroCPU efficiency
try:
    from ctransformers import AutoModelForCausalLM as AutoModelForCausalLM_GGUF
    # We still need AutoTokenizer from transformers for standard tokenizing
    from transformers import AutoTokenizer, AutoModelForCausalLM 
    GGUF_AVAILABLE = True
except ImportError:
    GGUF_AVAILABLE = False
    print("WARNING: 'ctransformers' not found. This app relies on it for efficient CPU inference.")
    print("Please install it with: pip install ctransformers transformers")
    # If ctransformers isn't available, we'll fall back to standard transformers loading, which is slower on CPU.
    from transformers import AutoTokenizer, AutoModelForCausalLM

# --- Configuration for Models and Generation ---
# Original model (for reference, or if a GPU is detected, though ZeroCPU is target)
ORIGINAL_MODEL_ID = "HuggingFaceTB/SmolLM2-360M-Instruct"

# !!! IMPORTANT !!! For efficient ZeroCPU (CPU-only) inference,
# a GGUF quantized model is HIGHLY RECOMMENDED.
# SmolLM2-360M-Instruct does NOT have a readily available GGUF version from common providers.
# Therefore, for ZeroCPU deployment, this app will use a common, small GGUF model by default.
# If you find a GGUF for SmolLM2 later, you can update these:
GGUF_MODEL_ID = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF" # Recommended GGUF placeholder for ZeroCPU
GGUF_MODEL_FILENAME = "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf" # Corresponding GGUF file name

# --- Generation Parameters ---
MAX_NEW_TOKENS = 256
TEMPERATURE = 0.7
TOP_K = 50
TOP_P = 0.95
DO_SAMPLE = True # Important for varied responses

# Global model and tokenizer variables
model = None
tokenizer = None
device = "cpu" # Explicitly set to CPU for ZeroCPU deployment

# --- Model Loading Function ---
def load_model_for_zerocpu():
    global model, tokenizer, device

    # Attempt to load the GGUF model first for efficiency on ZeroCPU
    if GGUF_AVAILABLE:
        print(f"Attempting to load GGUF model '{GGUF_MODEL_ID}' (file: '{GGUF_MODEL_FILENAME}') for ZeroCPU...")
        try:
            model = AutoModelForCausalLM_GGUF.from_pretrained(
                GGUF_MODEL_ID,
                model_file=GGUF_MODEL_FILENAME,
                model_type="llama", # Most GGUF models are Llama-based (TinyLlama is)
                gpu_layers=0 # Ensures it runs on CPU, not GPU
            )
            # Use the tokenizer from the original SmolLM2 for chat template consistency
            tokenizer = AutoTokenizer.from_pretrained(ORIGINAL_MODEL_ID)
            if tokenizer.pad_token is None:
                tokenizer.pad_token = tokenizer.eos_token
            print(f"GGUF model '{GGUF_MODEL_ID}' loaded successfully for CPU.")
            return # Exit function if GGUF model loaded successfully
        except Exception as e:
            print(f"WARNING: Could not load GGUF model '{GGUF_MODEL_ID}' from '{GGUF_MODEL_FILENAME}': {e}")
            print(f"Falling back to standard Hugging Face model '{ORIGINAL_MODEL_ID}' for CPU (will be slower without GGUF quantization).")
            # Continue to the next block to try loading the standard HF model
    else:
        print("WARNING: ctransformers is not available. Will load standard Hugging Face model directly.")
    
    # Fallback/alternative: Load the standard Hugging Face model (will be slower on CPU without GGUF)
    print(f"Loading standard Hugging Face model '{ORIGINAL_MODEL_ID}' for CPU...")
    try:
        model = AutoModelForCausalLM.from_pretrained(ORIGINAL_MODEL_ID)
        tokenizer = AutoTokenizer.from_pretrained(ORIGINAL_MODEL_ID)
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
        model.to(device) # Explicitly move model to CPU
        print(f"Standard model '{ORIGINAL_MODEL_ID}' loaded successfully on CPU.")
    except Exception as e:
        print(f"CRITICAL ERROR: Could not load standard model '{ORIGINAL_MODEL_ID}' on CPU: {e}")
        print("Please ensure the model ID is correct, you have enough RAM, and dependencies are installed.")
        model = None # Indicate failure to load
        tokenizer = None # Indicate failure to load

# --- Inference Function for Gradio ChatInterface ---
def predict_chat(message: str, history: list):
    # 'history' is a list of lists, where each inner list is [user_message, bot_message]
    # 'message' is the current user input

    if model is None or tokenizer is None:
        yield "Error: Model or tokenizer failed to load. Please check the Space logs for details."
        return

    # Build the full conversation history for the model's chat template
    messages = [{"role": "system", "content": "You are a friendly chatbot."}]
    for human_msg, ai_msg in history:
        messages.append({"role": "user", "content": human_msg})
        messages.append({"role": "assistant", "content": ai_msg})
    messages.append({"role": "user", "content": message}) # Add the current user message

    generated_text = ""
    
    start_time = time.time() # Start timing for the current turn

    if isinstance(model, AutoModelForCausalLM_GGUF): # Check if the loaded model is from ctransformers
        # For ctransformers (GGUF), manually construct a simple prompt string
        prompt_input = ""
        for msg in messages:
            if msg["role"] == "system":
                prompt_input += f"{msg['content']}\n"
            elif msg["role"] == "user":
                prompt_input += f"User: {msg['content']}\n"
            elif msg["role"] == "assistant":
                prompt_input += f"Assistant: {msg['content']}\n"
        prompt_input += "Assistant:" # Instruct the model to generate the assistant's response

        # Use the GGUF model's generate method
        for token in model.generate(
            prompt_input,
            max_new_tokens=MAX_NEW_TOKENS,
            temperature=TEMPERATURE,
            top_k=TOP_K,
            top_p=TOP_P,
            do_sample=DO_SAMPLE,
            repetition_penalty=1.1, # Common for GGUF models
            stop=["User:", "\nUser", "\n#", "\n##", "<|endoftext|>"] # Common stop tokens
        ):
            generated_text += token
            yield generated_text # Yield partial response for streaming in Gradio

    else: # If standard Hugging Face transformers model was loaded (slower on CPU)
        # Apply the tokenizer's chat template
        input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)

        # Generate the response
        outputs = model.generate(
            inputs,
            max_new_tokens=MAX_NEW_TOKENS,
            temperature=TEMPERATURE,
            top_k=TOP_K,
            top_p=TOP_P,
            do_sample=DO_SAMPLE,
            pad_token_id=tokenizer.pad_token_id # Important for generation
        )
        # Decode only the newly generated tokens
        generated_text = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True).strip()
        yield generated_text # Yield the full response at once (transformers.generate doesn't stream by default)
    
    end_time = time.time()
    print(f"Inference Time for this turn: {end_time - start_time:.2f} seconds")


# --- Gradio Interface Setup ---
if __name__ == "__main__":
    # Load the model globally when the Gradio app starts
    load_model_for_zerocpu()

    # Define a custom startup message for the chatbot
    initial_chatbot_message = (
        "Hello! I'm an AI assistant. I'm currently running in a CPU-only "
        "environment for efficient demonstration. How can I help you today?"
    )

    demo = gr.ChatInterface(
        fn=predict_chat, # The function that handles chat prediction
        chatbot=gr.Chatbot(height=500), # The chat display area
        textbox=gr.Textbox(
            placeholder="Ask me a question...",
            container=False,
            scale=7
        ),
        title="SmolLM2-360M-Instruct (or TinyLlama GGUF) on ZeroCPU",
        description=(
            f"This Space demonstrates an LLM for efficient CPU-only inference. "
            f"**Note:** For ZeroCPU, this app prioritizes `{GGUF_MODEL_ID}` (a GGUF-quantized model "
            f"like TinyLlama) due to better CPU performance than `{ORIGINAL_MODEL_ID}` "
            f"without GGUF. Expect varied responses each run due to randomized generation."
        ),
        theme="soft",
        examples=[ # Pre-defined examples for quick testing
            ["What is the capital of France?"],
            ["Can you tell me a fun fact about outer space?"],
            ["What's the best way to stay motivated?"],
        ],
        cache_examples=False, # Important: Ensures examples run inference each time, not from cache
        clear_btn="Clear Chat", # Button to clear the conversation
        # Custom message to start the conversation from the assistant
        initial_chatbot_message=initial_chatbot_message
    )

    # Launch the Gradio app
    # `share=True` creates a public link (useful for testing, but not needed on HF Spaces)
    # `server_name="0.0.0.0"` and `server_port=7860` are typically default for HF Spaces
    demo.launch()