import os import time import gc import sys import threading from itertools import islice from datetime import datetime import re # for parsing blocks import gradio as gr import torch from transformers import pipeline, TextIteratorStreamer, StoppingCriteria from transformers import AutoTokenizer from ddgs import DDGS import spaces # Import spaces early to enable ZeroGPU support from torch.utils._pytree import tree_map from config import * # Global event to signal cancellation from the UI thread to the generation thread cancel_event = threading.Event() access_token=os.environ['HF_TOKEN'] # Optional: Disable GPU visibility if you wish to force CPU usage # os.environ["CUDA_VISIBLE_DEVICES"] = "" # Global cache for pipelines to avoid re-loading. PIPELINES = {} def load_pipeline(model_name): """ Load and cache a transformers pipeline for text generation. Tries bfloat16, falls back to float16 or float32 if unsupported. """ global PIPELINES if model_name in PIPELINES: return PIPELINES[model_name] repo = MODELS[model_name]["repo_id"] tokenizer = AutoTokenizer.from_pretrained(repo, token=access_token) for dtype in (torch.bfloat16, torch.float16, torch.float32): try: pipe = pipeline( task="text-generation", model=repo, tokenizer=tokenizer, trust_remote_code=True, dtype=dtype, # Use `dtype` instead of deprecated `torch_dtype` device_map="auto", use_cache=True, # Enable past-key-value caching token=access_token) PIPELINES[model_name] = pipe return pipe except Exception: continue # Final fallback pipe = pipeline( task="text-generation", model=repo, tokenizer=tokenizer, trust_remote_code=True, device_map="auto", use_cache=True ) PIPELINES[model_name] = pipe return pipe def retrieve_context(query, max_results=6, max_chars=50): """ Retrieve search snippets from DuckDuckGo (runs in background). Returns a list of result strings. """ try: with DDGS() as ddgs: return [f"{i+1}. {r.get('title','No Title')} - {r.get('body','')[:max_chars]}" for i, r in enumerate(islice(ddgs.text(query, region="wt-wt", safesearch="off", timelimit="y"), max_results))] except Exception: return [] def format_conversation(history, system_prompt, tokenizer): if hasattr(tokenizer, "chat_template") and tokenizer.chat_template: messages = [{"role": "system", "content": system_prompt.strip()}] + history return tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=True) else: # Fallback for base LMs without chat template prompt = system_prompt.strip() + "\n" for msg in history: if msg['role'] == 'user': prompt += "User: " + msg['content'].strip() + "\n" elif msg['role'] == 'assistant': prompt += "Assistant: " + msg['content'].strip() + "\n" if not prompt.strip().endswith("Assistant:"): prompt += "Assistant: " return prompt def get_duration(user_msg, chat_history, system_prompt, enable_search, max_results, max_chars, model_name, max_tokens, temperature, top_k, top_p, repeat_penalty, search_timeout): # Get model size from the MODELS dict (more reliable than string parsing) model_size = MODELS[model_name].get("params_b", 4.0) # Default to 4B if not found # Only use AOT for models >= 2B parameters use_aot = model_size >= 2 # Adjusted for H200 performance: faster inference, quicker compilation base_duration = 20 if not use_aot else 40 # Reduced base times token_duration = max_tokens * 0.005 # ~200 tokens/second average on H200 search_duration = 10 if enable_search else 0 # Reduced search time aot_compilation_buffer = 20 if use_aot else 0 # Faster compilation on H200 return base_duration + token_duration + search_duration + aot_compilation_buffer @spaces.GPU(duration=get_duration) def chat_response(user_msg, chat_history, system_prompt, enable_search, max_results, max_chars, model_name, max_tokens, temperature, top_k, top_p, repeat_penalty, search_timeout): """ Generates streaming chat responses, optionally with background web search. This version includes cancellation support. """ # Clear the cancellation event at the start of a new generation cancel_event.clear() history = list(chat_history or []) history.append({'role': 'user', 'content': user_msg}) # Launch web search if enabled debug = '' search_results = [] if enable_search: debug = 'Search task started.' thread_search = threading.Thread( target=lambda: search_results.extend( retrieve_context(user_msg, int(max_results), int(max_chars)) ) ) thread_search.daemon = True thread_search.start() else: debug = 'Web search disabled.' try: cur_date = datetime.now().strftime('%Y-%m-%d') # merge any fetched search results into the system prompt if search_results: enriched = system_prompt.strip() + \ f'''\n# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: ''' else: enriched = system_prompt # wait up to 1s for snippets, then replace debug with them if enable_search: thread_search.join(timeout=float(search_timeout)) if search_results: debug = "### Search results merged into prompt\n\n" + "\n".join( f"- {r}" for r in search_results ) else: debug = "*No web search results found.*" # merge fetched snippets into the system prompt if search_results: enriched = system_prompt.strip() + \ f'''\n# The following contents are the search results related to the user's message: {search_results} In the search results I provide to you, each result is formatted as [webpage X begin]...[webpage X end], where X represents the numerical index of each article. Please cite the context at the end of the relevant sentence when appropriate. Use the citation format [citation:X] in the corresponding part of your answer. If a sentence is derived from multiple contexts, list all relevant citation numbers, such as [citation:3][citation:5]. Be sure not to cluster all citations at the end; instead, include them in the corresponding parts of the answer. When responding, please keep the following points in mind: - Today is {cur_date}. - Not all content in the search results is closely related to the user's question. You need to evaluate and filter the search results based on the question. - For listing-type questions (e.g., listing all flight information), try to limit the answer to 10 key points and inform the user that they can refer to the search sources for complete information. Prioritize providing the most complete and relevant items in the list. Avoid mentioning content not provided in the search results unless necessary. - For creative tasks (e.g., writing an essay), ensure that references are cited within the body of the text, such as [citation:3][citation:5], rather than only at the end of the text. You need to interpret and summarize the user's requirements, choose an appropriate format, fully utilize the search results, extract key information, and generate an answer that is insightful, creative, and professional. Extend the length of your response as much as possible, addressing each point in detail and from multiple perspectives, ensuring the content is rich and thorough. - If the response is lengthy, structure it well and summarize it in paragraphs. If a point-by-point format is needed, try to limit it to 5 points and merge related content. - For objective Q&A, if the answer is very brief, you may add one or two related sentences to enrich the content. - Choose an appropriate and visually appealing format for your response based on the user's requirements and the content of the answer, ensuring strong readability. - Your answer should synthesize information from multiple relevant webpages and avoid repeatedly citing the same webpage. - Unless the user requests otherwise, your response should be in the same language as the user's question. # The user's message is: ''' else: enriched = system_prompt pipe = load_pipeline(model_name) prompt = format_conversation(history, enriched, pipe.tokenizer) prompt_debug = f"\n\n--- Prompt Preview ---\n```\n{prompt}\n```" streamer = TextIteratorStreamer(pipe.tokenizer, skip_prompt=True, skip_special_tokens=True) gen_thread = threading.Thread( target=pipe, args=(prompt,), kwargs={ 'max_new_tokens': max_tokens, 'temperature': temperature, 'top_k': top_k, 'top_p': top_p, 'repetition_penalty': repeat_penalty, 'streamer': streamer, 'return_full_text': False, } ) gen_thread.start() # Buffers for thought vs answer thought_buf = '' answer_buf = '' in_thought = False assistant_message_started = False # First yield contains the user message yield history, debug # Stream tokens for chunk in streamer: # Check for cancellation signal if cancel_event.is_set(): if assistant_message_started and history and history[-1]['role'] == 'assistant': history[-1]['content'] += " [Generation Canceled]" yield history, debug break text = chunk # Detect start of thinking if not in_thought and '' in text: in_thought = True history.append({'role': 'assistant', 'content': '', 'metadata': {'title': '💭 Thought'}}) assistant_message_started = True after = text.split('', 1)[1] thought_buf += after if '' in thought_buf: before, after2 = thought_buf.split('', 1) history[-1]['content'] = before.strip() in_thought = False answer_buf = after2 history.append({'role': 'assistant', 'content': answer_buf}) else: history[-1]['content'] = thought_buf yield history, debug continue if in_thought: thought_buf += text if '' in thought_buf: before, after2 = thought_buf.split('', 1) history[-1]['content'] = before.strip() in_thought = False answer_buf = after2 history.append({'role': 'assistant', 'content': answer_buf}) else: history[-1]['content'] = thought_buf yield history, debug continue # Stream answer if not assistant_message_started: history.append({'role': 'assistant', 'content': ''}) assistant_message_started = True answer_buf += text history[-1]['content'] = answer_buf.strip() yield history, debug gen_thread.join() yield history, debug + prompt_debug except GeneratorExit: # Handle cancellation gracefully print("Chat response cancelled.") # Don't yield anything - let the cancellation propagate return except Exception as e: history.append({'role': 'assistant', 'content': f"Error: {e}"}) yield history, debug finally: gc.collect() def update_default_prompt(enable_search): return f"You are a helpful assistant." def update_duration_estimate(model_name, enable_search, max_results, max_chars, max_tokens, search_timeout): """Calculate and format the estimated GPU duration for current settings.""" try: dummy_msg, dummy_history, dummy_system_prompt = "", [], "" duration = get_duration(dummy_msg, dummy_history, dummy_system_prompt, enable_search, max_results, max_chars, model_name, max_tokens, 0.7, 40, 0.9, 1.2, search_timeout) model_size = MODELS[model_name].get("params_b", 4.0) return (f"⏱ī¸ **Estimated GPU Time: {duration:.1f} seconds**\n\n" f"📊 **Model Size:** {model_size:.1f}B parameters\n" f"🔍 **Web Search:** {'Enabled' if enable_search else 'Disabled'}") except Exception as e: return f"⚠ī¸ Error calculating estimate: {e}" # ------------------------------ # Gradio UI # ------------------------------ with gr.Blocks( title="LLM Inference", theme=gr.themes.Soft( primary_hue="blue", secondary_hue="blue", neutral_hue="slate", radius_size="lg", font=[gr.themes.GoogleFont("Syne"), "Arial", "sans-serif"] ), css=""" .duration-estimate { background: linear-gradient(135deg, #667eea15 0%, #764ba215 100%); border-left: 4px solid #667eea; padding: 12px; border-radius: 8px; margin: 16px 0; } .chatbot { border-radius: 12px; box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1); } button.primary { font-weight: 600; } .gradio-accordion { margin-bottom: 12px; } """ ) as demo: # Header gr.Markdown(""" # 🧠 CPU LLM Inference """) with gr.Row(): # Left Panel - Configuration with gr.Column(scale=3): # Core Settings (Always Visible) with gr.Group(): gr.Markdown("### ⚙ī¸ Core Settings") model_dd = gr.Dropdown( label="🤖 Model", choices=list(MODELS.keys()), value="Qwen3-1.7B", info="Select the language model to use" ) search_chk = gr.Checkbox( label="🔍 Enable Web Search", value=False, info="Augment responses with real-time web data" ) sys_prompt = gr.Textbox(label="📝 System Prompt", lines=3, value=update_default_prompt(search_chk.value), placeholder="Define the assistant's behavior and personality...") # Duration Estimate duration_display = gr.Markdown( value=update_duration_estimate("Qwen3-1.7B", False, 4, 50, 1024, 5.0), elem_classes="duration-estimate" ) # Advanced Settings (Collapsible) with gr.Accordion("🎛ī¸ Advanced Generation Parameters", open=False): max_tok = gr.Slider( 64, 16384, value=1024, step=32, label="Max Tokens", info="Maximum length of generated response" ) temp = gr.Slider( 0.1, 2.0, value=0.7, step=0.1, label="Temperature", info="Higher = more creative, Lower = more focused" ) with gr.Row(): k = gr.Slider( 1, 100, value=40, step=1, label="Top-K", info="Number of top tokens to consider" ) p = gr.Slider( 0.1, 1.0, value=0.9, step=0.05, label="Top-P", info="Nucleus sampling threshold" ) rp = gr.Slider( 1.0, 2.0, value=1.2, step=0.1, label="Repetition Penalty", info="Penalize repeated tokens" ) # Web Search Settings (Collapsible) with gr.Accordion("🌐 Web Search Settings", open=False, visible=False) as search_settings: mr = gr.Number( value=4, precision=0, label="Max Results", info="Number of search results to retrieve" ) mc = gr.Number( value=50, precision=0, label="Max Chars/Result", info="Character limit per search result" ) st = gr.Slider( minimum=0.0, maximum=30.0, step=0.5, value=5.0, label="Search Timeout (s)", info="Maximum time to wait for search results" ) # Actions with gr.Row(): clr = gr.Button("🗑ī¸ Clear Chat", variant="secondary", scale=1) # Right Panel - Chat Interface with gr.Column(scale=7): chat = gr.Chatbot( type="messages", height=600, label="đŸ’Ŧ Conversation", show_copy_button=True, avatar_images=(None, "🤖"), bubble_full_width=False ) # Input Area with gr.Row(): txt = gr.Textbox( placeholder="💭 Type your message here... (Press Enter to send)", scale=9, container=False, show_label=False, lines=1, max_lines=5 ) with gr.Column(scale=1, min_width=120): submit_btn = gr.Button("📤 Send", variant="primary", size="lg") cancel_btn = gr.Button("⏚ī¸ Stop", variant="stop", visible=False, size="lg") # Example Prompts gr.Examples( examples=[ ["Explain quantum computing in simple terms"], ["Write a Python function to calculate fibonacci numbers"], ["What are the latest developments in AI? (Enable web search)"], ["Tell me a creative story about a time traveler"], ["Help me debug this code: def add(a,b): return a+b+1"] ], inputs=txt, label="💡 Example Prompts" ) # Debug/Status Info (Collapsible) with gr.Accordion("🔍 Debug Info", open=False): dbg = gr.Markdown() # Footer gr.Markdown(""" --- 💡 **Tips:** - Use **Advanced Parameters** to fine-tune creativity and response length - Enable **Web Search** for real-time, up-to-date information - Try different **models** for various tasks (reasoning, coding, general chat) - Click the **Copy** button on responses to save them to your clipboard """, elem_classes="footer") # --- Event Listeners --- # Group all inputs for cleaner event handling chat_inputs = [txt, chat, sys_prompt, search_chk, mr, mc, model_dd, max_tok, temp, k, p, rp, st] # Group all UI components that can be updated. ui_components = [chat, dbg, txt, submit_btn, cancel_btn] def submit_and_manage_ui(user_msg, chat_history, *args): """ Orchestrator function that manages UI state and calls the backend chat function. It uses a try...finally block to ensure the UI is always reset. """ if not user_msg.strip(): # If the message is empty, do nothing. # We yield an empty dict to avoid any state changes. yield {} return # 1. Update UI to "generating" state. # Crucially, we do NOT update the `chat` component here, as the backend # will provide the correctly formatted history in the first response chunk. yield { txt: gr.update(value="", interactive=False), submit_btn: gr.update(interactive=False), cancel_btn: gr.update(visible=True), } cancelled = False try: # 2. Call the backend and stream updates backend_args = [user_msg, chat_history] + list(args) for response_chunk in chat_response(*backend_args): yield { chat: response_chunk[0], dbg: response_chunk[1], } except GeneratorExit: # Mark as cancelled and re-raise to prevent "generator ignored GeneratorExit" cancelled = True print("Generation cancelled by user.") raise except Exception as e: print(f"An error occurred during generation: {e}") # If an error happens, add it to the chat history to inform the user. error_history = (chat_history or []) + [ {'role': 'user', 'content': user_msg}, {'role': 'assistant', 'content': f"**An error occurred:** {str(e)}"} ] yield {chat: error_history} finally: # Only reset UI if not cancelled (to avoid "generator ignored GeneratorExit") if not cancelled: print("Resetting UI state.") yield { txt: gr.update(interactive=True), submit_btn: gr.update(interactive=True), cancel_btn: gr.update(visible=False), } def set_cancel_flag(): """Called by the cancel button, sets the global event.""" cancel_event.set() print("Cancellation signal sent.") def reset_ui_after_cancel(): """Reset UI components after cancellation.""" cancel_event.clear() # Clear the flag for next generation print("UI reset after cancellation.") return { txt: gr.update(interactive=True), submit_btn: gr.update(interactive=True), cancel_btn: gr.update(visible=False), } # Event for submitting text via Enter key or Submit button submit_event = txt.submit( fn=submit_and_manage_ui, inputs=chat_inputs, outputs=ui_components, ) submit_btn.click( fn=submit_and_manage_ui, inputs=chat_inputs, outputs=ui_components, ) # Event for the "Cancel" button. # It sets the cancel flag, cancels the submit event, then resets the UI. cancel_btn.click( fn=set_cancel_flag, cancels=[submit_event] ).then( fn=reset_ui_after_cancel, outputs=ui_components ) # Listeners for updating the duration estimate duration_inputs = [model_dd, search_chk, mr, mc, max_tok, st] for component in duration_inputs: component.change(fn=update_duration_estimate, inputs=duration_inputs, outputs=duration_display) # Toggle web search settings visibility def toggle_search_settings(enabled): return gr.update(visible=enabled) search_chk.change( fn=lambda enabled: (update_default_prompt(enabled), gr.update(visible=enabled)), inputs=search_chk, outputs=[sys_prompt, search_settings] ) # Clear chat action clr.click(fn=lambda: ([], "", ""), outputs=[chat, txt, dbg]) demo.launch()