Delete app.py

app.py

@@ -1,258 +0,0 @@
-"""
-Hugging Face Spaces Entry Point for CSRC Car Manual RAG System
-This is the entry point for Hugging Face Spaces deployment
-
-Note: For local development, use main.py instead.
-"""
-import os
-import sys
-from pathlib import Path
-
-# Detect if running in Hugging Face Spaces
-IS_SPACES = os.getenv("SPACE_ID") is not None or os.getenv("HF_SPACE") is not None
-
-# Add the current directory to Python path for Spaces environment
-sys.path.insert(0, str(Path(__file__).parent))
-
-from openai import OpenAI
-from src.config import Config
-from src.vector_store import VectorStoreManager
-from src.rag_query import RAGQueryEngine
-from src.question_generator import QuestionGenerator
-from src.knowledge_graph import KnowledgeGraphGenerator
-from src.gradio_interface import GradioInterfaceBuilder
-
-# Import personalized learning if available
-try:
-    from modules.personalized_learning import UserProfilingSystem, LearningPathGenerator, AdaptiveLearningEngine
-    PERSONALIZED_LEARNING_AVAILABLE = True
-except ImportError:
-    PERSONALIZED_LEARNING_AVAILABLE = False
-    print("⚠️ Personalized learning modules not available")
-
-# Import proactive learning if available
-try:
-    from modules.proactive_learning import ProactiveLearningEngine
-    PROACTIVE_LEARNING_AVAILABLE = True
-except ImportError:
-    PROACTIVE_LEARNING_AVAILABLE = False
-    print("⚠️ Proactive learning modules not available")
-
-# Import scenario contextualization if available
-try:
-    from modules.scenario_contextualization.database.scenario_database import ScenarioDatabase
-    from modules.scenario_contextualization.integration.feature_extractor import ADASFeatureExtractor
-    from modules.scenario_contextualization.retrieval.scenario_retriever import ScenarioRetriever
-    from modules.scenario_contextualization.formatting.constructive_formatter import ConstructiveFormatter
-    from modules.scenario_contextualization.integration.enhanced_rag_engine import EnhancedRAGEngine
-    SCENARIO_CONTEXTUALIZATION_AVAILABLE = True
-except ImportError as e:
-    SCENARIO_CONTEXTUALIZATION_AVAILABLE = False
-    print(f"⚠️ Scenario contextualization modules not available: {e}")
-
-
-def initialize_system(config: Config) -> dict:
-    """Initialize the RAG system components"""
-    # Initialize OpenAI client
-    if not config.openai_api_key:
-        raise ValueError(
-            "OPENAI_API_KEY not found! Please set it in Hugging Face Spaces Secrets. "
-            "Go to Settings > Secrets and add OPENAI_API_KEY"
-        )
-    
-    client = OpenAI(api_key=config.openai_api_key)
-    
-    # Initialize vector store manager
-    vector_store_manager = VectorStoreManager(client)
-    
-    # Get or create vector store
-    vector_store_id = config.get_vector_store_id()
-    
-    if not vector_store_id:
-        print("📦 Creating new vector store...")
-        pdf_files = config.get_pdf_files()
-        
-        if not pdf_files:
-            raise ValueError(f"No PDF files found in {config.car_manual_dir}")
-        
-        vector_store_details = vector_store_manager.create_vector_store(config.vector_store_name)
-        if not vector_store_details:
-            raise RuntimeError("Failed to create vector store")
-        
-        vector_store_id = vector_store_details["id"]
-        config.save_vector_store_id(vector_store_id, config.vector_store_name)
-        
-        # Upload files
-        upload_stats = vector_store_manager.upload_pdf_files(pdf_files, vector_store_id)
-        if upload_stats["successful_uploads"] == 0:
-            raise RuntimeError("Failed to upload any files")
-    else:
-        print(f"✅ Using existing vector store: {vector_store_id}")
-    
-    # Initialize RAG query engine
-    rag_engine = RAGQueryEngine(client, vector_store_id, config.model)
-    
-    # Initialize question generator
-    question_generator = QuestionGenerator(client, rag_engine)
-    
-    # Initialize knowledge graph generator
-    knowledge_graph = KnowledgeGraphGenerator(client, vector_store_id, str(config.output_dir))
-    
-    # Initialize personalized learning (if available)
-    user_profiling = None
-    learning_path_generator = None
-    adaptive_engine = None
-    
-    if PERSONALIZED_LEARNING_AVAILABLE:
-        try:
-            user_profiling = UserProfilingSystem()
-            learning_path_generator = LearningPathGenerator(user_profiling, config.available_topics)
-            adaptive_engine = AdaptiveLearningEngine(user_profiling, learning_path_generator)
-            print("✅ Personalized Learning System initialized!")
-        except Exception as e:
-            print(f"⚠️ Error initializing Personalized Learning System: {e}")
-    
-    # Initialize proactive learning (if available)
-    proactive_engine = None
-    if PROACTIVE_LEARNING_AVAILABLE and user_profiling:
-        try:
-            proactive_engine = ProactiveLearningEngine(
-                client, rag_engine, user_profiling, adaptive_engine, config.available_topics
-            )
-            print("✅ Proactive Learning Assistance initialized!")
-        except Exception as e:
-            print(f"⚠️ Error initializing Proactive Learning Assistance: {e}")
-    
-    # Initialize scenario contextualization (if available)
-    enhanced_rag_engine = None
-    if SCENARIO_CONTEXTUALIZATION_AVAILABLE:
-        try:
-            scenario_database = ScenarioDatabase()
-            feature_extractor = ADASFeatureExtractor(use_llm=False, client=client)
-            scenario_retriever = ScenarioRetriever(
-                scenario_database=scenario_database,
-                scenario_vector_store_id=None,
-                client=client
-            )
-            formatter = ConstructiveFormatter()
-            enhanced_rag_engine = EnhancedRAGEngine(
-                base_rag_engine=rag_engine,
-                scenario_retriever=scenario_retriever,
-                feature_extractor=feature_extractor,
-                formatter=formatter
-            )
-            print("✅ Scenario Contextualization initialized!")
-        except Exception as e:
-            print(f"⚠️ Error initializing Scenario Contextualization: {e}")
-            import traceback
-            traceback.print_exc()
-    
-    return {
-        "client": client,
-        "vector_store_manager": vector_store_manager,
-        "rag_engine": rag_engine,
-        "question_generator": question_generator,
-        "knowledge_graph": knowledge_graph,
-        "user_profiling": user_profiling,
-        "learning_path_generator": learning_path_generator,
-        "adaptive_engine": adaptive_engine,
-        "proactive_engine": proactive_engine,
-        "enhanced_rag_engine": enhanced_rag_engine,
-        "config": config
-    }
-
-
-def create_app():
-    """Create and return the Gradio app for Hugging Face Spaces"""
-    print("=" * 60)
-    print("🚗 CSRC Car Manual RAG System - Hugging Face Spaces")
-    print("=" * 60)
-    
-    # Load configuration
-    config = Config()
-    
-    # Initialize system
-    try:
-        components = initialize_system(config)
-    except Exception as e:
-        print(f"❌ Error initializing system: {e}")
-        import gradio as gr
-        
-        # Create error interface
-        error_msg = f"""
-        # ❌ Initialization Error
-        
-        **Error:** {str(e)}
-        
-        **Possible solutions:**
-        1. Check if OPENAI_API_KEY is set in Spaces Secrets (Settings > Secrets)
-        2. Ensure PDF files are in the `car_manual/` directory
-        3. Check the logs for more details
-        """
-        
-        def error_display():
-            return error_msg
-        
-        error_interface = gr.Interface(
-            fn=error_display,
-            inputs=None,
-            outputs=gr.Markdown(),
-            title="CSRC Car Manual RAG System",
-            description="An error occurred during initialization. Please check the logs."
-        )
-        return error_interface
-    
-    # Build Gradio interface
-    print("\n🌐 Building Gradio interface...")
-    interface_builder = GradioInterfaceBuilder(
-        rag_engine=components["rag_engine"],
-        question_generator=components["question_generator"],
-        knowledge_graph=components["knowledge_graph"],
-        config=components["config"],
-        user_profiling=components["user_profiling"],
-        adaptive_engine=components["adaptive_engine"],
-        proactive_engine=components["proactive_engine"]
-    )
-    
-    demo = interface_builder.create_interface()
-    return demo
-
-
-# Create the app for Hugging Face Spaces
-# Spaces will automatically detect Gradio and run this
-# Wrap in try-except to ensure demo is always defined
-try:
-    demo = create_app()
-except Exception as e:
-    print(f"❌ Fatal error creating app: {e}")
-    import traceback
-    traceback.print_exc()
-    
-    # Create a minimal error interface
-    import gradio as gr
-    
-    def show_error():
-        return f"""
-        # ❌ Application Error
-        
-        **Fatal Error:** {str(e)}
-        
-        Please check:
-        1. OPENAI_API_KEY is set in Spaces Secrets
-        2. All required files are uploaded
-        3. Check the logs for detailed error information
-        
-        **Traceback:**
-        ```
-        {traceback.format_exc()}
-        ```
-        """
-    
-    demo = gr.Interface(
-        fn=show_error,
-        inputs=None,
-        outputs=gr.Markdown(),
-        title="CSRC Car Manual RAG System - Error",
-        description="An error occurred. Please check the logs."
-    )
-