app.py

import subprocess  # 🥲
import os
import time
import torch
import numpy as np
import gradio as gr
import spaces
import re
import json
from datetime import datetime
from transformers import AutoModelForCausalLM, AutoTokenizer
from duckduckgo_search import DDGS
from pydantic import BaseModel

# ----------------------- Setup & Dependency Installation ----------------------- #
try:
    subprocess.run(['git', 'lfs', 'install'], check=True)
    if not os.path.exists('Kokoro-82M'):
        subprocess.run(['git', 'clone', 'https://huggingface.co/hexgrad/Kokoro-82M'], check=True)
    
    try:
        subprocess.run(['apt-get', 'update'], check=True)
        subprocess.run(['apt-get', 'install', '-y', 'espeak'], check=True)
    except subprocess.CalledProcessError:
        print("Warning: Could not install espeak. Trying espeak-ng...")
        try:
            subprocess.run(['apt-get', 'install', '-y', 'espeak-ng'], check=True)
        except subprocess.CalledProcessError:
            print("Warning: Could not install espeak or espeak-ng. TTS functionality may be limited.")
except Exception as e:
    print(f"Warning: Initial setup error: {str(e)}")
    print("Continuing with limited functionality...")

# ----------------------- Global Variables ----------------------- #
# 음성 관련 변수는 더 이상 사용하지 않음
# VOICE_CHOICES = { ... }  --> 제거

# ----------------------- Model and Tokenizer Initialization ----------------------- #
model_name = "deepseek-ai/DeepSeek-R1-Distill-Llama-8B"
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token

def init_models():
    model = AutoModelForCausalLM.from_pretrained(
        model_name,
        device_map="auto",
        offload_folder="offload",
        low_cpu_mem_usage=True,
        torch_dtype=torch.float16
    )
    return model

# ----------------------- Kokoro TTS Initialization ----------------------- #
# 음성 기능 제거: TTS 초기화 관련 코드는 더 이상 사용하지 않음
TTS_ENABLED = False

# ----------------------- Web Search Functions ----------------------- #
def get_web_results(query, max_results=5):
    try:
        with DDGS() as ddgs:
            results = list(ddgs.text(query, max_results=max_results))
            return [{
                "title": result.get("title", ""),
                "snippet": result["body"],
                "url": result["href"],
                "date": result.get("published", "")
            } for result in results]
    except Exception as e:
        return []

def format_prompt(query, context):
    """웹 검색 결과를 바탕으로 간결하고 요약된 답변을 생성하도록 프롬프트를 구성합니다."""
    current_time = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
    context_lines = '\n'.join([f'- {res["title"]}: {res["snippet"]}' for res in context])
    return f"""You are an intelligent search assistant. Your task is to provide a concise, clear summary answer to the user's query based solely on the provided web context.
Current Time: {current_time}

Query: {query}

Web Context:
{context_lines}

Please provide a summary answer in markdown format, including citations such as [1], [2], etc. in your answer if needed.
Answer:"""

def format_sources(web_results):
    if not web_results:
        return "<div class='no-sources'>No sources available</div>"
    
    sources_html = "<div class='sources-container'>"
    for i, res in enumerate(web_results, 1):
        title = res["title"] or "Source"
        date = f"<span class='source-date'>{res['date']}</span>" if res['date'] else ""
        sources_html += f"""
        <div class='source-item'>
            <div class='source-number'>[{i}]</div>
            <div class='source-content'>
                <a href="{res['url']}" target="_blank" class='source-title'>{title}</a>
                {date}
                <div class='source-snippet'>{res['snippet'][:150]}...</div>
            </div>
        </div>
        """
    sources_html += "</div>"
    return sources_html

# ----------------------- Answer Generation ----------------------- #
@spaces.GPU(duration=30)
def generate_answer(prompt):
    model = init_models()
    
    inputs = tokenizer(
        prompt, 
        return_tensors="pt", 
        padding=True,
        truncation=True,
        max_length=512,
        return_attention_mask=True
    ).to(model.device)
    
    outputs = model.generate(
        inputs.input_ids,
        attention_mask=inputs.attention_mask,
        max_new_tokens=256,
        temperature=0.7,
        top_p=0.95,
        pad_token_id=tokenizer.eos_token_id,
        do_sample=True,
        early_stopping=True
    )
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# ----------------------- Process Query and Output Summary ----------------------- #
def process_query(query, history):
    try:
        if history is None:
            history = []
            
        # 웹 검색 결과 가져오기
        web_results = get_web_results(query)
        sources_html = format_sources(web_results)
        
        # 중간 상태 표시
        current_history = history + [[query, "*Searching...*"]]
        yield {
            answer_output: gr.Markdown("*Searching & Summarizing...*"),
            sources_output: gr.HTML(sources_html),
            search_btn: gr.Button("Searching...", interactive=False),
            chat_history_display: current_history
        }
        
        # 프롬프트 생성: 웹 결과를 요약하는 형태로 구성
        prompt_text = format_prompt(query, web_results)
        answer = generate_answer(prompt_text)
        final_answer = answer.split("Answer:")[-1].strip()
        
        updated_history = history + [[query, final_answer]]
        yield {
            answer_output: gr.Markdown(final_answer),
            sources_output: gr.HTML(sources_html),
            search_btn: gr.Button("Search", interactive=True),
            chat_history_display: updated_history
        }
    except Exception as e:
        error_message = str(e)
        if "GPU quota" in error_message:
            error_message = "⚠️ GPU quota exceeded. Please try again later when the daily quota resets."
        
        yield {
            answer_output: gr.Markdown(f"Error: {error_message}"),
            sources_output: gr.HTML(""),
            search_btn: gr.Button("Search", interactive=True),
            chat_history_display: history + [[query, f"*Error: {error_message}*"]]
        }

# ----------------------- Custom CSS for Bright UI ----------------------- #
css = """
.gradio-container {
    max-width: 1200px !important;
    background-color: #ffffff !important;
    padding: 20px;
    border-radius: 12px;
}

#header {
    text-align: center;
    padding: 2rem 0;
    background: #e3f2fd;
    border-radius: 12px;
    color: #333333;
    margin-bottom: 2rem;
}

#header h1 {
    font-size: 2.5rem;
    margin-bottom: 0.5rem;
}

.search-container {
    background: #f8f9fa;
    border-radius: 12px;
    padding: 1.5rem;
    margin-bottom: 1rem;
    border: 1px solid #e0e0e0;
}

.search-box {
    padding: 1rem;
    background: #ffffff;
    border-radius: 8px;
    margin-bottom: 1rem;
    border: 1px solid #e0e0e0;
}

.search-box input[type="text"] {
    background: #ffffff !important;
    border: 1px solid #cccccc !important;
    color: #333333 !important;
    border-radius: 8px !important;
}

.search-box input[type="text"]::placeholder {
    color: #888888 !important;
}

.search-box button {
    background: #007bff !important;
    border: none !important;
}

.results-container {
    background: #ffffff;
    border-radius: 8px;
    padding: 1.5rem;
    margin-top: 1rem;
    border: 1px solid #e0e0e0;
}

.answer-box {
    background: #f1f1f1;
    border-radius: 8px;
    padding: 1.5rem;
    color: #333333;
    margin-bottom: 1rem;
}

.answer-box p {
    color: #555555;
    line-height: 1.6;
}

.sources-container {
    margin-top: 1rem;
    background: #ffffff;
    border-radius: 8px;
    padding: 1rem;
    border: 1px solid #e0e0e0;
}

.source-item {
    display: flex;
    padding: 12px;
    margin: 8px 0;
    background: #f8f9fa;
    border-radius: 8px;
    transition: all 0.2s;
}

.source-item:hover {
    background: #e9ecef;
}

.source-number {
    font-weight: bold;
    margin-right: 12px;
    color: #007bff;
}

.source-content {
    flex: 1;
}

.source-title {
    color: #007bff;
    font-weight: 500;
    text-decoration: none;
    display: block;
    margin-bottom: 4px;
}

.source-date {
    color: #888888;
    font-size: 0.9em;
    margin-left: 8px;
}

.source-snippet {
    color: #555555;
    font-size: 0.9em;
    line-height: 1.4;
}

.chat-history {
    max-height: 400px;
    overflow-y: auto;
    padding: 1rem;
    background: #ffffff;
    border-radius: 8px;
    margin-top: 1rem;
    border: 1px solid #e0e0e0;
}

footer {
    text-align: center;
    padding: 1rem 0;
    font-size: 0.9em;
    color: #666666;
}
"""

# ----------------------- Gradio Interface ----------------------- #
with gr.Blocks(title="AI Search Assistant", css=css) as demo:
    chat_history = gr.State([])
    
    with gr.Column(elem_id="header"):
        gr.Markdown("# 🔍 AI Search Assistant")
        gr.Markdown("### Powered by DeepSeek & Real-time Web Results")
    
    with gr.Column(elem_classes="search-container"):
        with gr.Row(elem_classes="search-box"):
            search_input = gr.Textbox(
                label="", 
                placeholder="Ask anything...", 
                scale=5,
                container=False
            )
            search_btn = gr.Button("Search", variant="primary", scale=1)
        
        with gr.Row(elem_classes="results-container"):
            with gr.Column(scale=2):
                with gr.Column(elem_classes="answer-box"):
                    answer_output = gr.Markdown()
                with gr.Accordion("Chat History", open=False):
                    chat_history_display = gr.Chatbot(elem_classes="chat-history")
            with gr.Column(scale=1):
                with gr.Column():
                    gr.Markdown("### Sources")
                    sources_output = gr.HTML()
        
        with gr.Row():
            gr.Examples(
                examples=[
                    "musk explores blockchain for doge",
                    "nvidia to launch new gaming card",
                    "What are the best practices for sustainable living?",
                    "How is climate change affecting ocean ecosystems?"
                ],
                inputs=search_input,
                label="Try these examples"
            )
    
    search_btn.click(
        fn=process_query,
        inputs=[search_input, chat_history],
        outputs=[answer_output, sources_output, search_btn, chat_history_display]
    )
    search_input.submit(
        fn=process_query,
        inputs=[search_input, chat_history],
        outputs=[answer_output, sources_output, search_btn, chat_history_display]
    )

if __name__ == "__main__":
    demo.launch(share=True)