app.py

import gradio as gr
import torch
import os
import time

# --- Try to import ctransformers for GGUF, provide helpful message if not found ---
try:
    from ctransformers import AutoModelForCausalLM as AutoModelForCausalLM_GGUF
    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")
    from transformers import AutoTokenizer, AutoModelForCausalLM

# --- Configuration for Models and Generation ---
ORIGINAL_MODEL_ID = "HuggingFaceTB/SmolLM2-360M-Instruct"
GGUF_MODEL_ID = "TheBloke/TinyLlama-1.1B-Chat-v1.0-GGUF" 
GGUF_MODEL_FILENAME = "tinyllama-1.1b-chat-v1.0.Q4_K_M.gguf" 

# --- Generation Parameters ---
MAX_NEW_TOKENS = 256
TEMPERATURE = 0.7
TOP_K = 50
TOP_P = 0.95
DO_SAMPLE = True 

# Global model and tokenizer
model = None
tokenizer = None
device = "cpu" 

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

    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", 
                gpu_layers=0 
            )
            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 
        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).")
    else:
        print("WARNING: ctransformers is not available. Will load standard Hugging Face model directly.")
    
    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) 
        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 
        tokenizer = None 

# --- Inference Function for Gradio ChatInterface ---
def predict_chat(message: str, history: list):
    if model is None or tokenizer is None:
        yield "Error: Model or tokenizer failed to load. Please check the Space logs for details."
        return

    # history contains [user_message, bot_message] tuples, convert to messages format for apply_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})

    generated_text = ""
    start_time = time.time()

    if isinstance(model, AutoModelForCausalLM_GGUF):
        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:"

        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,
            stop=["User:", "\nUser", "\n#", "\n##", "<|endoftext|>"] 
        ):
            generated_text += token
            yield generated_text 

    else:
        input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
        inputs = tokenizer.encode(input_text, return_tensors="pt").to(device)

        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
        )
        generated_text = tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True).strip()
        yield generated_text
    
    end_time = time.time()
    print(f"Inference Time for this turn: {end_time - start_time:.2f} seconds")

# --- Gradio Interface Setup ---
if __name__ == "__main__":
    load_model_for_zerocpu()

    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?"
    )

    # Use gr.Chatbot with type='messages' to avoid the deprecation warning
    chatbot_component = gr.Chatbot(height=500, type='messages') 
    
    with gr.Blocks(theme="soft") as demo: # Use gr.Blocks to lay out components
        gr.Markdown(
            f"# SmolLM2-360M-Instruct (or TinyLlama GGUF) on ZeroCPU\n"
            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."
        )
        
        chatbot_component.render() # Render the chatbot
        
        # Use gr.ChatInterface for the core chat functionality
        # It handles the textbox, send button, and history implicitly
        chat_interface = gr.ChatInterface(
            fn=predict_chat,
            chatbot=chatbot_component, # Link to the rendered chatbot component
            textbox=gr.Textbox(
                placeholder="Ask me a question...",
                container=False,
                scale=7
            ),
            # clear_btn is removed from ChatInterface constructor
            examples=[ 
                ["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, 
            # initial_chatbot_message will be set after chat_interface is rendered
        )
        
        # Manually add a clear button that links to the chatbot component
        gr.ClearButton(components=[chatbot_component]) 

        # Set the initial message for the chatbot
        # This needs to be done *after* the chatbot_component is defined
        chatbot_component.value = [[None, initial_chatbot_message]]


    demo.launch()