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 AutoModelForCausalLM, AutoTokenizer from huggingface_hub import hf_hub_download from duckduckgo_search import DDGS import spaces # ------------------------------ # Global Cancellation Event # ------------------------------ cancel_event = threading.Event() # ------------------------------ # Model Definitions and Global Variables (PyTorch/Transformers) # ------------------------------ # Here, the repo_id should point to a model checkpoint that is compatible with Hugging Face Transformers. # ------------------------------ # Torch-Compatible Model Definitions with Adjusted Descriptions # ------------------------------ MODELS = { "Taiwan-tinyllama-v1.0-chat (Q8_0)": { "repo_id": "DavidLanz/Taiwan-tinyllama-v1.0-chat", "description": "Taiwan-tinyllama-v1.0-chat (Q8_0) – Torch-compatible version converted from GGUF." }, "Llama-3.2-Taiwan-3B-Instruct (Q4_K_M)": { "repo_id": "https://huggingface.co/lianghsun/Llama-3.2-Taiwan-3B-Instruct", "description": "Llama-3.2-Taiwan-3B-Instruct (Q4_K_M) – Torch-compatible version converted from GGUF." }, "MiniCPM3-4B (Q4_K_M)": { "repo_id": "openbmb/MiniCPM3-4B", "description": "MiniCPM3-4B (Q4_K_M) – Torch-compatible version converted from GGUF." }, "Qwen2.5-3B-Instruct (Q4_K_M)": { "repo_id": "Qwen/Qwen2.5-3B-Instruct", "description": "Qwen2.5-3B-Instruct (Q4_K_M) – Torch-compatible version converted from GGUF." }, "Qwen2.5-7B-Instruct (Q2_K)": { "repo_id": "Qwen/Qwen2.5-7B-Instruct", "description": "Qwen2.5-7B-Instruct (Q2_K) – Torch-compatible version converted from GGUF." }, "Gemma-3-4B-IT (Q4_K_M)": { "repo_id": "unsloth/gemma-3-4b-it", "description": "Gemma-3-4B-IT (Q4_K_M) – Torch-compatible version converted from GGUF." }, "Phi-4-mini-Instruct (Q4_K_M)": { "repo_id": "unsloth/Phi-4-mini-instruct", "description": "Phi-4-mini-Instruct (Q4_K_M) – Torch-compatible version converted from GGUF." }, "Meta-Llama-3.1-8B-Instruct (Q2_K)": { "repo_id": "MaziyarPanahi/Meta-Llama-3.1-8B-Instruct", "description": "Meta-Llama-3.1-8B-Instruct (Q2_K) – Torch-compatible version converted from GGUF." }, "DeepSeek-R1-Distill-Llama-8B (Q2_K)": { "repo_id": "unsloth/DeepSeek-R1-Distill-Llama-8B", "description": "DeepSeek-R1-Distill-Llama-8B (Q2_K) – Torch-compatible version converted from GGUF." }, "Mistral-7B-Instruct-v0.3 (IQ3_XS)": { "repo_id": "MaziyarPanahi/Mistral-7B-Instruct-v0.3", "description": "Mistral-7B-Instruct-v0.3 (IQ3_XS) – Torch-compatible version converted from GGUF." }, "Qwen2.5-Coder-7B-Instruct (Q2_K)": { "repo_id": "Qwen/Qwen2.5-Coder-7B-Instruct", "description": "Qwen2.5-Coder-7B-Instruct (Q2_K) – Torch-compatible version converted from GGUF." }, } LOADED_MODELS = {} CURRENT_MODEL_NAME = None # ------------------------------ # Model Loading Helper Function (PyTorch/Transformers) # ------------------------------ def load_model(model_name): global LOADED_MODELS, CURRENT_MODEL_NAME if model_name in LOADED_MODELS: return LOADED_MODELS[model_name] selected_model = MODELS[model_name] # Load both the model and tokenizer using the Transformers library. model = AutoModelForCausalLM.from_pretrained(selected_model["repo_id"], trust_remote_code=True) tokenizer = AutoTokenizer.from_pretrained(selected_model["repo_id"], trust_remote_code=True) LOADED_MODELS[model_name] = (model, tokenizer) CURRENT_MODEL_NAME = model_name return model, tokenizer # ------------------------------ # Web Search Context Retrieval Function # ------------------------------ def retrieve_context(query, max_results=6, max_chars_per_result=600): 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 (Simulated Streaming) with Cancellation # ------------------------------ @spaces.GPU 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): # Reset the cancellation event. cancel_event.clear() # Prepare internal history. internal_history = list(chat_history) if chat_history else [] internal_history.append({"role": "user", "content": user_message}) # Retrieve web search context (with debug feedback). debug_message = "" if enable_search: debug_message = "Initiating web search..." yield internal_history, 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}" else: debug_message = "Web search returned no results or timed out." else: retrieved_context = "" debug_message = "Web search disabled." # Augment prompt with search context if available. if enable_search and retrieved_context: augmented_user_input = ( f"{system_prompt.strip()}\n\n" "Use the following recent web search context to help answer the query:\n\n" f"{retrieved_context}\n\n" f"User Query: {user_message}" ) else: augmented_user_input = f"{system_prompt.strip()}\n\nUser Query: {user_message}" # Append a placeholder for the assistant's response. internal_history.append({"role": "assistant", "content": ""}) try: # Load the PyTorch model and tokenizer. model, tokenizer = load_model(model_name) # Tokenize the input prompt. input_ids = tokenizer(augmented_user_input, return_tensors="pt").input_ids with torch.no_grad(): output_ids = model.generate( input_ids, max_new_tokens=max_tokens, temperature=temperature, top_k=top_k, top_p=top_p, repetition_penalty=repeat_penalty, do_sample=True ) # Decode the generated tokens. generated_text = tokenizer.decode(output_ids[0], skip_special_tokens=True) # Strip the original prompt to isolate the assistant’s reply. assistant_text = generated_text[len(augmented_user_input):].strip() # Simulate streaming by yielding the output word by word. words = assistant_text.split() assistant_message = "" for word in words: if cancel_event.is_set(): assistant_message += "\n\n[Response generation cancelled by user]" internal_history[-1]["content"] = assistant_message yield internal_history, debug_message return assistant_message += word + " " internal_history[-1]["content"] = assistant_message yield internal_history, debug_message time.sleep(0.05) # Short delay to simulate streaming except Exception as e: internal_history[-1]["content"] = f"Error: {e}" yield internal_history, debug_message 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) 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()