app.py

import os
import time
import gc
import sys
import threading
from itertools import islice
from datetime import datetime
import re
import gradio as gr
import torch
from transformers import pipeline, TextIteratorStreamer
from transformers import AutoTokenizer
from ddgs import DDGS
from config import MODELS  # Import from config file

# Global event to signal cancellation from the UI thread to the generation thread
cancel_event = threading.Event()

access_token = os.environ.get('HF_TOKEN', '')

# 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,
                device_map="auto",
                use_cache=True,
                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
    model_size = MODELS[model_name].get("params_b", 4.0)
    
    # Only use AOT for models >= 2B parameters
    use_aot = model_size >= 2
    
    # Adjusted for H200 performance
    base_duration = 20 if not use_aot else 40
    token_duration = max_tokens * 0.005
    search_duration = 10 if enable_search else 0
    aot_compilation_buffer = 20 if use_aot else 0
    
    return base_duration + token_duration + search_duration + aot_compilation_buffer

def get_model_size(model_name):
    """Get model size from the MODELS dict."""
    return MODELS.get(model_name, {}).get("params_b", 4.0)

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.'

    # Wait for search results if enabled
    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.*"

    try:
        cur_date = datetime.now().strftime('%Y-%m-%d')
        
        # Prepare enriched system prompt
        if search_results:
            enriched = system_prompt.strip() + f"""
# SEARCH CONTEXT (TRUSTED SOURCES ONLY)
Below are web search results. Treat them as the ONLY source of truth for answering.
{search_results}

RULES (VERY IMPORTANT):
- Do NOT use outside knowledge. Do NOT guess or fill missing information.
- If the answer is not clearly supported by the search results, say: "Not enough information in the provided sources."
- Every factual statement must be directly supported by at least one citation [citation:X].
- Do NOT add explanations, examples, or background that are not explicitly present in the sources.
- Do NOT paraphrase beyond what is necessary for clarity.
- If sources conflict, mention the conflict and cite both.
- If multiple sources are used, distribute citations per sentence, not only at the end.

CITATION RULES:
- Use inline citations like this: [citation:1]
- If multiple sources support a sentence: [citation:1][citation:3]
- Never place all citations only at the end.

ANSWER POLICY:
- Be concise and strictly grounded.
- No speculation, no assumptions, no "likely", no "probably".
- If the user requests a list, only include items explicitly found in sources.
- If sources are insufficient, stop and ask for more data instead of guessing.

DATE CONTEXT:
- Today is {cur_date} (use only for time reference, not for assumptions).

USER QUESTION:
"""
        else:
            enriched = system_prompt.strip()

        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 '<think>' in text:
                in_thought = True
                history.append({'role': 'assistant', 'content': '', 'metadata': {'title': '💭 Thought'}})
                assistant_message_started = True
                after = text.split('<think>', 1)[1]
                thought_buf += after
                if '</think>' in thought_buf:
                    before, after2 = thought_buf.split('</think>', 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 '</think>' in thought_buf:
                    before, after2 = thought_buf.split('</think>', 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.")
        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 = get_model_size(model_name)
        return (f"⏱️ **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"⚠️ 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("### ⚙️ 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(False), 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("🎛️ 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("🗑️ Clear Chat", variant="secondary", scale=1)
        
        # Right Panel - Chat Interface
        with gr.Column(scale=7):
            chat = gr.Chatbot(
                type="messages",
                height=600,
                label="💬 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("⏹️ 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():
            yield {}
            return

        # Update UI to "generating" state
        yield {
            txt: gr.update(value="", interactive=False),
            submit_btn: gr.update(interactive=False),
            cancel_btn: gr.update(visible=True),
        }

        cancelled = False
        try:
            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:
            cancelled = True
            print("Generation cancelled by user.")
            raise
        except Exception as e:
            print(f"An error occurred during generation: {e}")
            error_history = (chat_history or []) + [
                {'role': 'user', 'content': user_msg},
                {'role': 'assistant', 'content': f"**An error occurred:** {str(e)}"}
            ]
            yield {chat: error_history}
        finally:
            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()
        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.
    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()