app.py

import gradio as gr
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
import os

# --- Configuration ---
# IMPORTANT: Replace with the path to your locally downloaded model or a Hugging Face model ID.
# Examples:
# LOCAL_MODEL_PATH = "/path/to/your/downloaded/qwen-1.5b-instruct"
# HUGGINGFACE_MODEL_ID = "Qwen/Qwen1.5-1.8B-Chat" # For a smaller Qwen model for local testing
HUGGINGFACE_MODEL_ID = "."

# You might need to adjust TORCH_DTYPE based on your GPU and model support
# torch.float16 (FP16) is common for inference, torch.bfloat16 for newer GPUs
TORCH_DTYPE = torch.float16 # or torch.bfloat16 or torch.float32

# Generation parameters (can be adjusted for different response styles)
MAX_NEW_TOKENS = 512
DO_SAMPLE = True
TEMPERATURE = 0.7
TOP_K = 50
TOP_P = 0.95

# --- Global variables for models and tokenizers ---
tokenizer = None
model = None

# --- Load Models and Tokenizers Function ---
def load_model_and_tokenizer():
    """
    Loads the language model and tokenizer from Hugging Face Hub or a local path.
    This function will be called once when the Gradio app starts up.
    """
    global tokenizer, model

    if tokenizer is not None and model is not None:
        print("Model and tokenizer already loaded.")
        return

    print(f"Loading tokenizer from: {HUGGINGFACE_MODEL_ID}")
    try:
        tokenizer = AutoTokenizer.from_pretrained(HUGGINGFACE_MODEL_ID)
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token
            print(f"Set tokenizer.pad_token to tokenizer.eos_token ({tokenizer.pad_token_id})")

        print(f"Loading model from: {HUGGINGFACE_MODEL_ID}...")
        model = AutoModelForCausalLM.from_pretrained(
            HUGGINGFACE_MODEL_ID,
            torch_dtype=TORCH_DTYPE,
            device_map="auto" # Automatically maps model to GPU if available, else CPU
        )
        model.eval() # Set model to evaluation mode
        print("Model loaded successfully.")
    except Exception as e:
        print(f"Error loading model or tokenizer: {e}")
        print("Please ensure the model ID is correct and you have an internet connection for initial download, or the local path is valid.")
        tokenizer = None
        model = None
        raise RuntimeError("Failed to load model. Check your model ID/path and internet connection.")


# --- Generate Response Function ---
def generate_response(
    message: str, # Current user message
    history: list # Gradio Chatbot history format (list of dictionaries with 'role' and 'content')
) -> list: # Returns updated history for the Chatbot
    """
    Generates a text response from the loaded model based on user input and chat history.
    """
    global tokenizer, model

    # Initialize models if not already loaded
    if tokenizer is None or model is None:
        load_model_and_tokenizer()

    if tokenizer is None or model is None: # Check again in case loading failed
        # history.append([message, "Error: Chatbot model not loaded. Please check logs."])
        # For 'messages' type history, append a dictionary
        history.append({"role": "user", "content": message})
        history.append({"role": "assistant", "content": "Error: Chatbot model not loaded. Please check logs."})
        return history

    # Format messages for the model's chat template (e.g., for Instruct models)
    # The 'history' now directly contains dictionaries if type='messages' is used.
    messages = history # Use history directly as it's already in the correct format
    messages.append({"role": "user", "content": message}) # Add current user message

    # Apply the chat template and tokenize
    try:
        input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
    except Exception as e:
        print(f"Error applying chat template: {e}")
        # Fallback if chat template fails (e.g., for non-chat models)
        # Reconstruct input_text for models without explicit chat templates
        input_text = ""
        for item in history:
            if item["role"] == "user":
                input_text += f"User: {item['content']}\n"
            elif item["role"] == "assistant":
                input_text += f"Assistant: {item['content']}\n"
        input_text += f"User: {message}\nAssistant:"

    input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to(model.device)

    # Generate response
    with torch.no_grad(): # Disable gradient calculations for inference
        output_ids = model.generate(
            input_ids,
            max_new_tokens=MAX_NEW_TOKENS,
            do_sample=DO_SAMPLE,
            temperature=TEMPERATURE,
            top_k=TOP_K,
            top_p=TOP_P,
            pad_token_id=tokenizer.eos_token_id # Important for generation to stop cleanly
        )

    # Decode the generated text, excluding the input prompt part
    generated_token_ids = output_ids[0][input_ids.shape[-1]:]
    generated_text = tokenizer.decode(generated_token_ids, skip_special_tokens=True).strip()

    # --- Update Chat History ---
    # Append the latest generated response to the history with its role
    history.append({"role": "assistant", "content": generated_text})

    return history

# --- Gradio Interface ---
with gr.Blocks() as demo:
    gr.Markdown(
        """
        # Local Chatbot Powered by Hugging Face Transformers
        Type your message below and chat with the model loaded locally on your machine!
        """
    )

    # Set type='messages' for the chatbot to use OpenAI-style dictionaries
    chatbot = gr.Chatbot(label="Conversation", type='messages')
    with gr.Row():
        text_input = gr.Textbox(
            label="Your message",
            placeholder="Type your message here...",
            scale=4
        )
        submit_button = gr.Button("Send", scale=1)

    # Link the text input and button to the generation function
    # Note: 'inputs' will be current message and the full history (as 'messages' type)
    # 'outputs' will be the updated full history
    submit_button.click(
        fn=generate_response,
        inputs=[text_input, chatbot], # text_input is the new message, chatbot is the history
        outputs=[chatbot],
        queue=True # Queue requests for better concurrency
    )
    text_input.submit( # Also trigger on Enter key
        fn=generate_response,
        inputs=[text_input, chatbot],
        outputs=[chatbot],
        queue=True
    )

    # Clear button
    def clear_chat():
        # When type='messages', the clear function should return an empty list for history
        # and an empty string for the text input.
        return [], ""
    clear_button = gr.Button("Clear Chat")
    clear_button.click(clear_chat, inputs=None, outputs=[chatbot, text_input])


# Load the model when the app starts. This will ensure it's ready when the first request comes in.
load_model_and_tokenizer()

# Launch the Gradio app
#demo.queue().launch() # For local development, use launch()
demo.queue().launch(server_name="0.0.0.0")