app.py

import os
import time
import gc
import threading
from itertools import islice
from datetime import datetime
import gradio as gr
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from duckduckgo_search import DDGS
import spaces  # Import spaces early to enable ZeroGPU support

# Optional: Disable GPU visibility if you wish to force CPU usage
# os.environ["CUDA_VISIBLE_DEVICES"] = ""

# ------------------------------
# Global Cancellation Event
# ------------------------------
cancel_event = threading.Event()

# ------------------------------
# Torch-Compatible Model Definitions with Adjusted Descriptions
# ------------------------------
MODELS = {
    "Taiwan-tinyllama-v1.0-chat": {
        "repo_id": "DavidLanz/Taiwan-tinyllama-v1.0-chat",
        "description": "Taiwan-tinyllama-v1.0-chat"
    },
    "Llama-3.2-Taiwan-3B-Instruct": {
        "repo_id": "https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct",
        "description": "Llama-3.2-Taiwan-3B-Instruct"
    },
    "MiniCPM3-4B": {
        "repo_id": "openbmb/MiniCPM3-4B",
        "description": "MiniCPM3-4B"
    },
    "Qwen2.5-3B-Instruct": {
        "repo_id": "Qwen/Qwen2.5-3B-Instruct",
        "description": "Qwen2.5-3B-Instruct"
    },
    "Qwen2.5-7B-Instruct": {
        "repo_id": "Qwen/Qwen2.5-7B-Instruct",
        "description": "Qwen2.5-7B-Instruct"
    },
    "Gemma-3-4B-IT": {
        "repo_id": "unsloth/gemma-3-4b-it",
        "description": "Gemma-3-4B-IT"
    },
    "Phi-4-mini-Instruct": {
        "repo_id": "unsloth/Phi-4-mini-instruct",
        "description": "Phi-4-mini-Instruct"
    },
    "Meta-Llama-3.1-8B-Instruct": {
        "repo_id": "MaziyarPanahi/Meta-Llama-3.1-8B-Instruct",
        "description": "Meta-Llama-3.1-8B-Instruct"
    },
    "DeepSeek-R1-Distill-Llama-8B": {
        "repo_id": "unsloth/DeepSeek-R1-Distill-Llama-8B",
        "description": "DeepSeek-R1-Distill-Llama-8B"
    },
    "Mistral-7B-Instruct-v0.3": {
        "repo_id": "MaziyarPanahi/Mistral-7B-Instruct-v0.3",
        "description": "Mistral-7B-Instruct-v0.3"
    },
    "Qwen2.5-Coder-7B-Instruct": {
        "repo_id": "Qwen/Qwen2.5-Coder-7B-Instruct",
        "description": "Qwen2.5-Coder-7B-Instruct"
    },
}

# Global cache for pipelines to avoid re-loading.
PIPELINES = {}

def load_pipeline(model_name):
    """
    Load and cache a transformers pipeline for chat/text-generation.
    Uses the model's repo_id from MODELS and caches the pipeline for future use.
    """
    global PIPELINES
    if model_name in PIPELINES:
        return PIPELINES[model_name]
    selected_model = MODELS[model_name]
    # Create a chat-style text-generation pipeline.
    pipe = pipeline(
        task="text-generation",
        model=selected_model["repo_id"],
        tokenizer=selected_model["repo_id"],
        trust_remote_code=True,
        torch_dtype=torch.bfloat16,
        device_map="auto"
    )
    PIPELINES[model_name] = pipe
    return pipe

def retrieve_context(query, max_results=6, max_chars_per_result=600):
    """
    Retrieve recent web search context for the given query using DuckDuckGo.
    Returns a formatted string with search results.
    """
    try:
        with DDGS() as ddgs:
            results = list(islice(ddgs.text(query, region="wt-wt", safesearch="off", timelimit="y"), max_results))
            context = ""
            for i, result in enumerate(results, start=1):
                title = result.get("title", "No Title")
                snippet = result.get("body", "")[:max_chars_per_result]
                context += f"Result {i}:\nTitle: {title}\nSnippet: {snippet}\n\n"
            return context.strip()
    except Exception:
        return ""

# ------------------------------
# Chat Response Generation with ZeroGPU using Pipeline
# ------------------------------
@spaces.GPU(duration=60)
def chat_response(user_message, chat_history, system_prompt, enable_search,
                  max_results, max_chars, model_name, max_tokens, temperature, top_k, top_p, repeat_penalty):
    """
    Generate a chat response by utilizing a transformers pipeline.
    
    - Appends the user's message to the conversation history.
    - Optionally retrieves web search context and inserts it as an additional system message.
    - Uses a cached pipeline (loaded via load_pipeline) to generate a response.
    - Returns the updated conversation history and a debug message.
    """
    cancel_event.clear()
    
    # Build conversation list from chat history.
    conversation = list(chat_history) if chat_history else []
    conversation.append({"role": "user", "content": user_message})
    
    # Retrieve web search context if enabled.
    debug_message = ""
    retrieved_context = ""
    if enable_search:
        debug_message = "Initiating web search..."
        yield conversation, debug_message
        search_result = [""]
        def do_search():
            search_result[0] = retrieve_context(user_message, max_results, max_chars)
        search_thread = threading.Thread(target=do_search)
        search_thread.start()
        search_thread.join(timeout=2)
        retrieved_context = search_result[0]
        if retrieved_context:
            debug_message = f"Web search results:\n\n{retrieved_context}"
            # Insert the search context as a system-level message immediately after the original system prompt.
            conversation.insert(1, {"role": "system", "content": f"Web search context:\n{retrieved_context}"})
        else:
            debug_message = "Web search returned no results or timed out."
    else:
        debug_message = "Web search disabled."
    
    # Append a placeholder for the assistant's response.
    conversation.append({"role": "assistant", "content": ""})
    
    try:
        # Load the pipeline (cached) for the selected model.
        pipe = load_pipeline(model_name)
        
        # Use the pipeline directly with conversation history.
        # Note: Many chat pipelines use internal chat templating to properly format the conversation.
        response = pipe(
            conversation,
            max_new_tokens=max_tokens,
            temperature=temperature,
            top_k=top_k,
            top_p=top_p,
            repetition_penalty=repeat_penalty,
        )
        # Extract the assistant's reply.
        try:
            assistant_text = response[0]["generated_text"][-1]["content"]
        except (KeyError, IndexError, TypeError):
            assistant_text = response[0]["generated_text"]
        
        # Update the conversation history.
        conversation[-1]["content"] = assistant_text
        
        # Yield the complete conversation history and the debug message.
        yield conversation, debug_message
    except Exception as e:
        conversation[-1]["content"] = f"Error: {e}"
        yield conversation, debug_message
    finally:
        gc.collect()

# ------------------------------
# Cancel Function
# ------------------------------
def cancel_generation():
    cancel_event.set()
    return "Cancellation requested."

# ------------------------------
# Gradio UI Definition
# ------------------------------
with gr.Blocks(title="LLM Inference with ZeroGPU") as demo:
    gr.Markdown("## 🧠 ZeroGPU LLM Inference with Web Search")
    gr.Markdown("Interact with the model. Select your model, set your system prompt, and adjust parameters on the left.")
    
    with gr.Row():
        with gr.Column(scale=3):
            default_model = list(MODELS.keys())[0] if MODELS else "No models available"
            model_dropdown = gr.Dropdown(
                label="Select Model", 
                choices=list(MODELS.keys()) if MODELS else [], 
                value=default_model,
                info="Choose from available models."
            )
            today = datetime.now().strftime('%Y-%m-%d')
            default_prompt = f"You are a helpful assistant. Today is {today}. Please leverage the latest web data when responding to queries."
            system_prompt_text = gr.Textbox(label="System Prompt",
                                            value=default_prompt,
                                            lines=3,
                                            info="Define the base context for the AI's responses.")
            gr.Markdown("### Generation Parameters")
            max_tokens_slider = gr.Slider(label="Max Tokens", minimum=64, maximum=1024, value=1024, step=32,
                                          info="Maximum tokens for the response.")
            temperature_slider = gr.Slider(label="Temperature", minimum=0.1, maximum=2.0, value=0.7, step=0.1,
                                           info="Controls the randomness of the output.")
            top_k_slider = gr.Slider(label="Top-K", minimum=1, maximum=100, value=40, step=1,
                                     info="Limits token candidates to the top-k tokens.")
            top_p_slider = gr.Slider(label="Top-P (Nucleus Sampling)", minimum=0.1, maximum=1.0, value=0.95, step=0.05,
                                     info="Limits token candidates to a cumulative probability threshold.")
            repeat_penalty_slider = gr.Slider(label="Repetition Penalty", minimum=1.0, maximum=2.0, value=1.1, step=0.1,
                                              info="Penalizes token repetition to improve diversity.")
            gr.Markdown("### Web Search Settings")
            enable_search_checkbox = gr.Checkbox(label="Enable Web Search", value=False,
                                                 info="Include recent search context to improve answers.")
            max_results_number = gr.Number(label="Max Search Results", value=6, precision=0,
                                           info="Maximum number of search results to retrieve.")
            max_chars_number = gr.Number(label="Max Chars per Result", value=600, precision=0,
                                         info="Maximum characters to retrieve per search result.")
            clear_button = gr.Button("Clear Chat")
            cancel_button = gr.Button("Cancel Generation")
        with gr.Column(scale=7):
            chatbot = gr.Chatbot(label="Chat", type="messages")
            msg_input = gr.Textbox(label="Your Message", placeholder="Enter your message and press Enter")
            search_debug = gr.Markdown(label="Web Search Debug")
    
    def clear_chat():
        return [], "", ""
    
    clear_button.click(fn=clear_chat, outputs=[chatbot, msg_input, search_debug])
    cancel_button.click(fn=cancel_generation, outputs=search_debug)
    
    # Submission: the chat_response function is now used with the Transformers pipeline.
    msg_input.submit(
        fn=chat_response,
        inputs=[msg_input, chatbot, system_prompt_text, enable_search_checkbox,
                max_results_number, max_chars_number, model_dropdown,
                max_tokens_slider, temperature_slider, top_k_slider, top_p_slider, repeat_penalty_slider],
        outputs=[chatbot, search_debug],
    )
    
demo.launch()