Create app.py

app.py

@@ -0,0 +1,247 @@
+"""
+Gradio app for Multi-Document RAG Assistant
+(Auto-loads documents from data/ directory)
+"""
+
+import gradio as gr
+from backend.processing import process_documents_from_directory, get_available_files
+from backend.rag import RAGEngine
+from backend.llm import LLMClient
+
+# -------------------------------
+# Global state
+# -------------------------------
+rag_engine = RAGEngine()
+llm_client = LLMClient()
+
+# -------------------------------
+# Auto-initialize on startup
+# -------------------------------
+def initialize_system():
+    """Initialize the system by loading documents from data/ directory."""
+    try:
+        available_files = get_available_files("data")
+        if not available_files:
+            return "⚠️ No documents found in data/ directory. Please add PDF, TXT, or MD files to the data folder.", []
+        
+        print(f"📁 Found {len(available_files)} files: {available_files}")
+        
+        # Check if we already have an index with these files
+        if rag_engine.get_chunk_count() > 0:
+            return f"✅ Using existing index with {rag_engine.get_chunk_count()} chunks", available_files
+        
+        # Process and index documents
+        chunks = process_documents_from_directory("data")
+        if chunks:
+            rag_engine.add_documents(chunks)
+            return f"✅ Ready! Indexed {len(chunks)} chunks from {len(available_files)} documents.", available_files
+        else:
+            return "⚠️ No valid content extracted from documents", available_files
+    except Exception as e:
+        return f"❌ Error initializing system: {str(e)}", []
+
+# Initialize system on startup
+system_status, loaded_files = initialize_system()
+print(f"System Status: {system_status}")
+
+# -------------------------------
+# Rebuild index function
+# -------------------------------
+def rebuild_index():
+    """Rebuild the index from data/ directory."""
+    try:
+        chunk_count = rag_engine.rebuild_from_data("data")
+        available_files = get_available_files("data")
+        if chunk_count > 0:
+            status = f"✅ Rebuilt index with {chunk_count} chunks from {len(available_files)} files"
+        else:
+            status = "⚠️ No documents found to index"
+        return status, chunk_count, available_files
+    except Exception as e:
+        return f"❌ Error rebuilding index: {str(e)}", 0, []
+
+# -------------------------------
+# Search & generate answer
+# -------------------------------
+def search_and_answer(question, top_k, history):
+    if not question.strip():
+        return history, ""
+
+    if rag_engine.get_chunk_count() == 0:
+        error_msg = "⚠️ No documents loaded. Please add PDF, TXT, or MD files to the 'data/' directory and click 'Rebuild Index'."
+        history.append({"role": "user", "content": question})
+        history.append({"role": "assistant", "content": error_msg})
+        return history, ""
+
+    try:
+        # Search for relevant chunks
+        results = rag_engine.search(question, top_k=top_k)
+
+        if not results:
+            no_results_msg = "⚠️ No relevant information found in the documents for this question."
+            history.append({"role": "user", "content": question})
+            history.append({"role": "assistant", "content": no_results_msg})
+            return history, ""
+
+        # Generate answer
+        answer = llm_client.generate_answer(question, results)
+
+        # Add to chat history
+        history.append({"role": "user", "content": question})
+        history.append({"role": "assistant", "content": answer})
+
+        return history, ""
+    
+    except Exception as e:
+        error_msg = f"❌ Error processing question: {str(e)}"
+        history.append({"role": "user", "content": question})
+        history.append({"role": "assistant", "content": error_msg})
+        return history, ""
+
+def get_system_info():
+    """Get current system information."""
+    current_files = get_available_files("data")
+    chunk_count = rag_engine.get_chunk_count()
+    
+    info = f"""
+**📊 System Status**
+
+**📁 Documents in data/ folder:** {len(current_files)}
+{chr(10).join([f"• {file}" for file in current_files]) if current_files else "• None"}
+
+**🧠 Chunks Indexed:** {chunk_count}
+
+**🤖 LLM Status:** {"✅ Azure OpenAI configured" if llm_client.has_token() else "⚠️ No Azure OpenAI token (using extractive fallback)"}
+
+**💡 Usage:** Ask questions about the content in your documents. The system searches through all indexed chunks to provide relevant answers.
+"""
+    return info
+
+# -------------------------------
+# UI - Clean Chat Interface
+# -------------------------------
+with gr.Blocks(
+    title="AI Document Assistant", 
+    theme=gr.themes.Soft(),
+    css="""
+    .gradio-container {
+        max-width: 1200px !important;
+        margin: auto;
+    }
+    """
+) as demo:
+    
+    # Header
+    gr.Markdown("""
+    # 🤖 AI Document Assistant
+    
+    Ask questions about your documents. The system automatically loads all documents from the `data/` directory.
+    """)
+    
+    # System info and controls
+    with gr.Accordion("📊 System Information & Controls", open=False):
+        system_info = gr.Markdown(get_system_info())
+        
+        with gr.Row():
+            refresh_info_btn = gr.Button("🔄 Refresh Info", variant="secondary")
+            rebuild_btn = gr.Button("🔨 Rebuild Index", variant="secondary")
+        
+        rebuild_status = gr.Markdown()
+    
+    # Main chat interface
+    chatbot = gr.Chatbot(
+        type="messages", 
+        height=500,
+        show_label=False,
+        container=True,
+        show_copy_button=True
+    )
+    
+    # Input area
+    with gr.Row():
+        question = gr.Textbox(
+            placeholder="Ask a question about your documents...",
+            label="Your Question",
+            scale=4,
+            lines=1,
+            max_lines=3
+        )
+        
+        submit_btn = gr.Button("💬 Send", variant="primary", scale=1)
+    
+    # Advanced options
+    with gr.Accordion("⚙️ Advanced Settings", open=False):
+        top_k = gr.Slider(
+            minimum=1,
+            maximum=10,
+            value=5,
+            step=1,
+            label="Number of document chunks to retrieve",
+            info="Higher values provide more context but may include less relevant information"
+        )
+        
+        clear_btn = gr.Button("🗑️ Clear Chat History", variant="secondary")
+
+    # -------------------------------
+    # Event handlers
+    # -------------------------------
+    
+    # Submit on button click
+    submit_btn.click(
+        search_and_answer,
+        inputs=[question, top_k, chatbot],
+        outputs=[chatbot, question]
+    )
+    
+    # Submit on Enter key
+    question.submit(
+        search_and_answer,
+        inputs=[question, top_k, chatbot],
+        outputs=[chatbot, question]
+    )
+    
+    # Clear chat history
+    clear_btn.click(
+        lambda: [],
+        outputs=[chatbot]
+    )
+    
+    # Refresh system info
+    refresh_info_btn.click(
+        get_system_info,
+        outputs=[system_info]
+    )
+    
+    # Rebuild index
+    rebuild_btn.click(
+        rebuild_index,
+        outputs=[rebuild_status, system_info, system_info]  # Update both status and info
+    )
+    
+    # Show welcome message if system is ready
+    if rag_engine.get_chunk_count() > 0:
+        demo.load(
+            lambda: [{
+                "role": "assistant", 
+                "content": f"👋 **Welcome to AI Document Assistant!**\n\nI'm ready to help you with questions about your documents. I have access to **{rag_engine.get_chunk_count()} chunks** of information from **{len(loaded_files)} documents**:\n\n" + 
+                "\n".join([f"📄 {file}" for file in loaded_files]) + 
+                f"\n\n💡 **What would you like to know?** You can ask about specific topics, request summaries, or explore relationships between different documents."
+            }],
+            outputs=[chatbot]
+        )
+    else:
+        demo.load(
+            lambda: [{
+                "role": "assistant", 
+                "content": "⚠️ **No documents loaded.**\n\nTo get started:\n1. Create a `data/` folder in your project directory\n2. Add PDF, TXT, or MD files to the folder\n3. Click '🔨 Rebuild Index' or restart the application\n\nI'll automatically load and index all your documents for instant searching!"
+            }],
+            outputs=[chatbot]
+        )
+
+# -------------------------------
+# Launch
+# -------------------------------
+if __name__ == "__main__":
+    demo.launch(
+        debug=True
+    )