Delete app.py

app.py

@@ -1,486 +0,0 @@
-#!/usr/bin/env python3
-"""
-Just search - A Smart Search Agent using Menlo/Lucy-128k
-Part of the Just, AKA Simple series
-Built with Gradio, DuckDuckGo Search, and Hugging Face Transformers
-"""
-
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
-from duckduckgo_search import DDGS
-import json
-import re
-import time
-from typing import List, Dict, Tuple
-import spaces
-
-# Initialize the model and tokenizer globally for efficiency
-MODEL_NAME = "Menlo/Lucy-128k"
-tokenizer = None
-model = None
-search_pipeline = None
-
-def initialize_model():
-    """Initialize the Menlo/Lucy-128k model and tokenizer"""
-    global tokenizer, model, search_pipeline
-    try:
-        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, trust_remote_code=True)
-        model = AutoModelForCausalLM.from_pretrained(
-            MODEL_NAME,
-            torch_dtype=torch.float16,
-            device_map="auto",
-            trust_remote_code=True
-        )
-        search_pipeline = pipeline(
-            "text-generation",
-            model=model,
-            tokenizer=tokenizer,
-            torch_dtype=torch.float16,
-            device_map="auto",
-            max_new_tokens=2048,
-            temperature=0.7,
-            do_sample=True,
-            pad_token_id=tokenizer.eos_token_id
-        )
-        return True
-    except Exception as e:
-        print(f"Error initializing model: {e}")
-        return False
-
-def clean_response(text: str) -> str:
-    """Clean up the AI response to extract just the relevant content"""
-    # Remove common prefixes and artifacts
-    text = re.sub(r'^(Assistant:|AI:|Response:|Answer:)\s*', '', text.strip())
-    text = re.sub(r'\[INST\].*?\[\/INST\]', '', text)
-    text = re.sub(r'<\|.*?\|>', '', text)
-    return text.strip()
-
-@spaces.GPU
-def generate_search_queries(user_query: str) -> List[str]:
-    """Generate multiple search queries based on user input using AI"""
-    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-You are a search query generator. Given a user's question, generate 3-5 different search queries that would help find comprehensive information to answer their question. Return only the search queries, one per line, without numbering or bullet points.
-
-Example:
-User: "What are the latest developments in AI?"
-latest AI developments 2024
-artificial intelligence breakthroughs recent
-AI technology advances news
-machine learning innovations 2024
-
-<|eot_id|><|start_header_id|>user<|end_header_id|>
-{user_query}
-<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-    try:
-        response = search_pipeline(prompt, max_new_tokens=200, temperature=0.3)
-        generated_text = response[0]['generated_text']
-        
-        # Extract just the assistant's response
-        assistant_response = generated_text.split('<|start_header_id|>assistant<|end_header_id|>')[-1]
-        assistant_response = clean_response(assistant_response)
-        
-        # Split into individual queries and clean them
-        queries = [q.strip() for q in assistant_response.split('\n') if q.strip()]
-        # Filter out any non-query text
-        queries = [q for q in queries if len(q) > 5 and not q.startswith('Note:') and not q.startswith('Example:')]
-        
-        return queries[:5]  # Return max 5 queries
-    except Exception as e:
-        print(f"Error generating queries: {e}")
-        # Fallback to simple query variations
-        return [user_query, f"{user_query} 2024", f"{user_query} latest"]
-
-def search_web(queries: List[str]) -> List[Dict]:
-    """Search the web using DuckDuckGo with multiple queries"""
-    all_results = []
-    ddgs = DDGS()
-    
-    for query in queries:
-        try:
-            results = ddgs.text(query, max_results=5, region='wt-wt', safesearch='moderate')
-            for result in results:
-                result['search_query'] = query
-                all_results.append(result)
-            time.sleep(0.5)  # Rate limiting
-        except Exception as e:
-            print(f"Error searching for '{query}': {e}")
-            continue
-    
-    # Remove duplicates based on URL
-    seen_urls = set()
-    unique_results = []
-    for result in all_results:
-        if result['href'] not in seen_urls:
-            seen_urls.add(result['href'])
-            unique_results.append(result)
-    
-    return unique_results[:15]  # Return max 15 results
-
-@spaces.GPU
-def filter_relevant_results(user_query: str, search_results: List[Dict]) -> List[Dict]:
-    """Use AI to filter and rank search results by relevance"""
-    if not search_results:
-        return []
-    
-    # Prepare results summary for AI
-    results_text = ""
-    for i, result in enumerate(search_results[:12]):  # Limit to avoid token overflow
-        results_text += f"{i+1}. Title: {result.get('title', 'No title')}\n"
-        results_text += f"   URL: {result.get('href', 'No URL')}\n"
-        results_text += f"   Snippet: {result.get('body', 'No description')[:200]}...\n\n"
-    
-    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-You are a search result evaluator. Given a user's question and search results, identify which results are most relevant and helpful for answering the question. 
-
-Return only the numbers of the most relevant results (1-5 results maximum), separated by commas. Consider:
-- Direct relevance to the question
-- Credibility of the source
-- Recency of information
-- Comprehensiveness of content
-
-Example response: 1, 3, 7
-
-<|eot_id|><|start_header_id|>user<|end_header_id|>
-Question: {user_query}
-
-Search Results:
-{results_text}
-
-<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-    try:
-        response = search_pipeline(prompt, max_new_tokens=100, temperature=0.1)
-        generated_text = response[0]['generated_text']
-        
-        # Extract assistant's response
-        assistant_response = generated_text.split('<|start_header_id|>assistant<|end_header_id|>')[-1]
-        assistant_response = clean_response(assistant_response)
-        
-        # Extract numbers
-        numbers = re.findall(r'\d+', assistant_response)
-        selected_indices = [int(n) - 1 for n in numbers if int(n) <= len(search_results)]
-        
-        return [search_results[i] for i in selected_indices if 0 <= i < len(search_results)][:5]
-    except Exception as e:
-        print(f"Error filtering results: {e}")
-        return search_results[:5]  # Fallback to first 5 results
-
-@spaces.GPU
-def generate_final_answer(user_query: str, selected_results: List[Dict]) -> str:
-    """Generate final answer based on selected search results"""
-    if not selected_results:
-        return "I couldn't find relevant information to answer your question. Please try rephrasing your query."
-    
-    # Prepare context from selected results
-    context = ""
-    for i, result in enumerate(selected_results):
-        context += f"Source {i+1}: {result.get('title', 'Unknown')}\n"
-        context += f"Content: {result.get('body', 'No content available')}\n"
-        context += f"URL: {result.get('href', 'No URL')}\n\n"
-    
-    prompt = f"""<|begin_of_text|><|start_header_id|>system<|end_header_id|>
-You are a helpful research assistant. Based on the provided search results, give a comprehensive answer to the user's question. 
-
-Guidelines:
-- Synthesize information from multiple sources
-- Be accurate and factual
-- Cite sources when possible
-- If information is conflicting, mention it
-- Keep the answer well-structured and easy to read
-- Include relevant URLs for further reading
-
-<|eot_id|><|start_header_id|>user<|end_header_id|>
-Question: {user_query}
-
-Search Results:
-{context}
-
-Please provide a comprehensive answer based on these sources.
-
-<|eot_id|><|start_header_id|>assistant<|end_header_id|>"""
-
-    try:
-        response = search_pipeline(prompt, max_new_tokens=1024, temperature=0.2)
-        generated_text = response[0]['generated_text']
-        
-        # Extract assistant's response
-        assistant_response = generated_text.split('<|start_header_id|>assistant<|end_header_id|>')[-1]
-        answer = clean_response(assistant_response)
-        
-        return answer
-    except Exception as e:
-        print(f"Error generating final answer: {e}")
-        return "I encountered an error while processing the search results. Please try again."
-
-def search_agent_workflow(user_query: str, progress=gr.Progress()) -> Tuple[str, str]:
-    """Main workflow that orchestrates the search agent"""
-    if not user_query.strip():
-        return "Please enter a search query.", ""
-    
-    progress(0.1, desc="Initializing...")
-    
-    # Step 1: Generate search queries
-    progress(0.2, desc="Generating search queries...")
-    queries = generate_search_queries(user_query)
-    queries_text = "Generated queries:\n" + "\n".join(f"• {q}" for q in queries)
-    
-    # Step 2: Search the web
-    progress(0.4, desc="Searching the web...")
-    search_results = search_web(queries)
-    
-    if not search_results:
-        return "No search results found. Please try a different query.", queries_text
-    
-    # Step 3: Filter relevant results
-    progress(0.6, desc="Filtering relevant results...")
-    relevant_results = filter_relevant_results(user_query, search_results)
-    
-    # Step 4: Generate final answer
-    progress(0.8, desc="Generating comprehensive answer...")
-    final_answer = generate_final_answer(user_query, relevant_results)
-    
-    progress(1.0, desc="Complete!")
-    
-    # Prepare debug info
-    debug_info = f"{queries_text}\n\nSelected {len(relevant_results)} relevant sources:\n"
-    for i, result in enumerate(relevant_results):
-        debug_info += f"{i+1}. {result.get('title', 'No title')} - {result.get('href', 'No URL')}\n"
-    
-    return final_answer, debug_info
-
-# Custom CSS for dark blue theme and mobile responsiveness
-custom_css = """
-/* Dark blue theme */
-:root {
-    --primary-bg: #0a1628;
-    --secondary-bg: #1e3a5f;
-    --accent-bg: #2563eb;
-    --text-primary: #f8fafc;
-    --text-secondary: #cbd5e1;
-    --border-color: #334155;
-    --input-bg: #1e293b;
-    --button-bg: #3b82f6;
-    --button-hover: #2563eb;
-}
-
-/* Global styles */
-.gradio-container {
-    background: linear-gradient(135deg, var(--primary-bg) 0%, var(--secondary-bg) 100%) !important;
-    color: var(--text-primary) !important;
-    font-family: 'Inter', 'Segoe UI', system-ui, sans-serif !important;
-}
-
-/* Mobile responsiveness */
-@media (max-width: 768px) {
-    .gradio-container {
-        padding: 10px !important;
-    }
-    
-    .gr-form {
-        gap: 15px !important;
-    }
-    
-    .gr-button {
-        font-size: 16px !important;
-        padding: 12px 20px !important;
-    }
-}
-
-/* Input styling */
-.gr-textbox textarea, .gr-textbox input {
-    background: var(--input-bg) !important;
-    border: 1px solid var(--border-color) !important;
-    color: var(--text-primary) !important;
-    border-radius: 8px !important;
-}
-
-/* Button styling */
-.gr-button {
-    background: linear-gradient(135deg, var(--button-bg) 0%, var(--accent-bg) 100%) !important;
-    color: white !important;
-    border: none !important;
-    border-radius: 8px !important;
-    font-weight: 600 !important;
-    transition: all 0.3s ease !important;
-}
-
-.gr-button:hover {
-    background: linear-gradient(135deg, var(--button-hover) 0%, var(--button-bg) 100%) !important;
-    transform: translateY(-1px) !important;
-    box-shadow: 0 4px 12px rgba(59, 130, 246, 0.3) !important;
-}
-
-/* Output styling */
-.gr-markdown, .gr-textbox {
-    background: var(--input-bg) !important;
-    border: 1px solid var(--border-color) !important;
-    border-radius: 8px !important;
-    color: var(--text-primary) !important;
-}
-
-/* Header styling */
-.gr-markdown h1 {
-    color: var(--accent-bg) !important;
-    text-align: center !important;
-    margin-bottom: 20px !important;
-    font-size: 2.5rem !important;
-    font-weight: 700 !important;
-}
-
-/* Loading animation */
-.gr-loading {
-    background: var(--secondary-bg) !important;
-    border-radius: 8px !important;
-}
-
-/* Scrollbar styling */
-::-webkit-scrollbar {
-    width: 8px;
-}
-
-::-webkit-scrollbar-track {
-    background: var(--primary-bg);
-}
-
-::-webkit-scrollbar-thumb {
-    background: var(--accent-bg);
-    border-radius: 4px;
-}
-
-::-webkit-scrollbar-thumb:hover {
-    background: var(--button-hover);
-}
-"""
-
-def create_interface():
-    """Create the Gradio interface"""
-    with gr.Blocks(
-        theme=gr.themes.Base(
-            primary_hue="blue",
-            secondary_hue="slate",
-            neutral_hue="slate",
-            text_size="lg",
-            spacing_size="lg",
-            radius_size="md"
-        ).set(
-            body_background_fill="*primary_950",
-            body_text_color="*neutral_50",
-            background_fill_primary="*primary_900",
-            background_fill_secondary="*primary_800",
-            border_color_primary="*primary_700",
-            button_primary_background_fill="*primary_600",
-            button_primary_background_fill_hover="*primary_500",
-            button_primary_text_color="white",
-            input_background_fill="*primary_800",
-            input_border_color="*primary_600",
-            input_text_color="*neutral_50"
-        ),
-        css=custom_css,
-        title="Just search - AI Search Agent",
-        head="<meta name='viewport' content='width=device-width, initial-scale=1.0'>"
-    ) as interface:
-        
-        gr.Markdown("# 🔍 Just search", elem_id="header")
-        gr.Markdown(
-            "*Part of the Just, AKA Simple series*\n\n"
-            "**Intelligent search agent powered by Menlo/Lucy-128k**\n\n"
-            "Ask any question and get comprehensive answers from the web.",
-            elem_id="description"
-        )
-        
-        with gr.Row():
-            with gr.Column(scale=4):
-                query_input = gr.Textbox(
-                    label="Your Question",
-                    placeholder="Ask me anything... (e.g., 'What are the latest developments in AI?')",
-                    lines=2,
-                    elem_id="query-input"
-                )
-            with gr.Column(scale=1):
-                search_btn = gr.Button(
-                    "🔎 Search",
-                    variant="primary",
-                    size="lg",
-                    elem_id="search-button"
-                )
-        
-        with gr.Row():
-            answer_output = gr.Markdown(
-                label="Answer",
-                elem_id="answer-output",
-                height=400
-            )
-        
-        with gr.Accordion("🔧 Debug Info", open=False):
-            debug_output = gr.Textbox(
-                label="Search Process Details",
-                lines=8,
-                elem_id="debug-output"
-            )
-        
-        # Event handlers
-        search_btn.click(
-            fn=search_agent_workflow,
-            inputs=[query_input],
-            outputs=[answer_output, debug_output],
-            show_progress=True
-        )
-        
-        query_input.submit(
-            fn=search_agent_workflow,
-            inputs=[query_input],
-            outputs=[answer_output, debug_output],
-            show_progress=True
-        )
-        
-        # Example queries
-        gr.Examples(
-            examples=[
-                ["What are the latest breakthroughs in quantum computing?"],
-                ["How does climate change affect ocean currents?"],
-                ["What are the best practices for sustainable agriculture?"],
-                ["Explain the recent developments in renewable energy technology"],
-                ["What are the health benefits of the Mediterranean diet?"]
-            ],
-            inputs=query_input,
-            outputs=[answer_output, debug_output],
-            fn=search_agent_workflow,
-            cache_examples=False
-        )
-        
-        gr.Markdown(
-            "---\n**Note:** This search agent generates multiple queries, searches the web, "
-            "filters results for relevance, and provides comprehensive answers. "
-            "Results are sourced from DuckDuckGo search."
-        )
-    
-    return interface
-
-def main():
-    """Main function to initialize and launch the app"""
-    print("🚀 Initializing Just search...")
-    
-    # Initialize the model
-    if not initialize_model():
-        print("❌ Failed to initialize model. Please check your setup.")
-        return
-    
-    print("✅ Model initialized successfully!")
-    print("🌐 Creating interface...")
-    
-    # Create and launch the interface
-    interface = create_interface()
-    
-    print("🎉 Just search is ready!")
-    interface.launch(
-        server_name="0.0.0.0",
-        server_port=7860,
-        share=True,
-        show_error=True,
-        debug=True
-    )
-
-if __name__ == "__main__":
-    main()