Create gradio.app

gradio.app

@@ -0,0 +1,215 @@
+import gradio as gr
+import requests
+import json
+import os
+from typing import List, Tuple
+
+# --- Configuration ---
+API_URL = "https://monocopter.net"
+API_TOKEN = os.getenv("API_TOKEN", "")
+
+HEADERS = {
+    "Authorization": f"Bearer {API_TOKEN}",
+    "Content-Type": "application/json"
+}
+
+# --- Core API Functions ---
+def query_rag_system(query: str, max_cards: int = 5) -> dict:
+    """Query the RAG system with a historical question."""
+    try:
+        payload = {"query": query, "max_cards": max_cards, "include_sources": True}
+        response = requests.post(f"{API_URL}/query", json=payload, headers=HEADERS)
+        return response.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+def search_cards_semantic(query: str, max_cards: int = 5) -> dict:
+    """Perform semantic search on cards."""
+    try:
+        payload = {"query": query, "max_cards": max_cards}
+        response = requests.post(f"{API_URL}/search", json=payload, headers=HEADERS)
+        return response.json()
+    except Exception as e:
+        return {"error": str(e)}
+
+# --- Chatbot Functions ---
+def format_rag_response(result: dict) -> str:
+    """Format the RAG response for display in the chatbot."""
+    if "error" in result:
+        return f"❌ **Error**: {result['error']}"
+    
+    answer = result.get("answer", "I couldn't generate an answer for your question.")
+    cards_used = result.get("cards_used", [])
+    sources = result.get("sources", [])
+    processing_time = result.get("processing_time", 0)
+    
+    # Build formatted response
+    response = f"**Answer:**\n{answer}\n\n"
+    
+    if sources:
+        response += f"**📚 Sources ({len(sources)} documents):**\n"
+        for i, source in enumerate(sources[:3], 1):  # Limit to top 3 sources
+            response += f"{i}. {source}\n"
+        if len(sources) > 3:
+            response += f"... and {len(sources) - 3} more sources\n"
+        response += "\n"
+    
+    if cards_used:
+        response += f"**🗃️ Retrieved {len(cards_used)} relevant cards** | "
+    response += f"**⏱️ {processing_time:.2f}s**"
+    
+    return response
+
+def format_search_response(result: dict) -> str:
+    """Format the search response for display."""
+    if "error" in result:
+        return f"❌ **Error**: {result['error']}"
+    
+    cards = result.get("cards", [])
+    if not cards:
+        return "🔍 No relevant historical information found for your search."
+    
+    response = f"🔍 **Found {len(cards)} relevant historical entries:**\n\n"
+    
+    for i, card in enumerate(cards[:3], 1):  # Show top 3 results
+        title = card.get('title', 'Untitled')
+        summary = card.get('summary', 'No summary available')
+        relevance = card.get('relevance_score', 0)
+        
+        # Truncate summary if too long
+        if len(summary) > 200:
+            summary = summary[:200] + "..."
+        
+        response += f"**{i}. {title}** (Relevance: {relevance:.3f})\n{summary}\n\n"
+    
+    if len(cards) > 3:
+        response += f"*... and {len(cards) - 3} more results*\n"
+    
+    processing_time = result.get("processing_time", 0)
+    response += f"⏱️ Search completed in {processing_time:.2f}s"
+    
+    return response
+
+def chatbot_respond(message: str, history: List[Tuple[str, str]], search_mode: bool) -> Tuple[str, List[Tuple[str, str]]]:
+    """Main chatbot response function."""
+    if not message.strip():
+        return "", history
+    
+    # Determine max cards based on query complexity
+    max_cards = 3 if len(message.split()) < 5 else 5
+    
+    if search_mode:
+        # Use semantic search for browsing/exploring
+        result = search_cards_semantic(message, max_cards)
+        response = format_search_response(result)
+    else:
+        # Use RAG for question-answering
+        result = query_rag_system(message, max_cards)
+        response = format_rag_response(result)
+    
+    # Add to history
+    history.append((message, response))
+    return "", history
+
+def clear_chat():
+    """Clear the chat history."""
+    return [], ""
+
+# --- Gradio Interface ---
+def create_interface():
+    with gr.Blocks(
+        title="Historical Knowledge Assistant",
+        theme=gr.themes.Soft(),
+        css="""
+        .chat-container { max-height: 600px; overflow-y: auto; }
+        .examples { margin: 10px 0; }
+        """
+    ) as demo:
+        
+        gr.Markdown("""
+        # 🏛️ Historical Knowledge Assistant
+        Ask questions about historical events, people, and concepts. Powered by your Rolodex RAG database.
+        """)
+        
+        with gr.Row():
+            with gr.Column(scale=4):
+                chatbot = gr.Chatbot(
+                    label="Historical Q&A",
+                    height=500,
+                    show_copy_button=True,
+                    container=True,
+                    elem_classes=["chat-container"]
+                )
+                
+                with gr.Row():
+                    msg_input = gr.Textbox(
+                        placeholder="Ask about historical events, people, or concepts...",
+                        label="Your Question",
+                        lines=2,
+                        scale=4
+                    )
+                    
+                with gr.Row():
+                    send_btn = gr.Button("💬 Ask", variant="primary", scale=1)
+                    clear_btn = gr.Button("🗑️ Clear", variant="secondary", scale=1)
+                    
+            with gr.Column(scale=1):
+                search_mode = gr.Checkbox(
+                    label="🔍 Search Mode",
+                    value=False,
+                    info="Toggle between Q&A (off) and Search (on)"
+                )
+                
+                gr.Markdown("""
+                ### 💡 Example Questions
+                **Question & Answer Mode:**
+                - What caused the American Revolution?
+                - How did colonial resistance evolve?
+                - Who were key figures in Bacon's Rebellion?
+                
+                **Search Mode:**
+                - colonial resistance
+                - Boston Massacre
+                - taxation without representation
+                
+                ### ℹ️ Tips
+                - **Q&A Mode**: Ask complete questions for detailed answers
+                - **Search Mode**: Use keywords to explore topics
+                - Sources and processing time shown with each response
+                """, elem_classes=["examples"])
+        
+        # Event handlers
+        msg_input.submit(
+            chatbot_respond,
+            inputs=[msg_input, chatbot, search_mode],
+            outputs=[msg_input, chatbot]
+        )
+        
+        send_btn.click(
+            chatbot_respond,
+            inputs=[msg_input, chatbot, search_mode],
+            outputs=[msg_input, chatbot]
+        )
+        
+        clear_btn.click(
+            clear_chat,
+            outputs=[chatbot, msg_input]
+        )
+    
+    return demo
+
+# --- Launch Configuration ---
+if __name__ == "__main__":
+    print("🚀 Launching Historical Knowledge Assistant...")
+    print(f"🌐 API URL: {API_URL}")
+    print(f"🔑 Using API token: {API_TOKEN[:10]}...")
+    
+    demo = create_interface()
+    
+    # For Hugging Face Spaces deployment
+    demo.launch(
+        share=False,  # Set to True for temporary sharing
+        server_name="0.0.0.0",  # Required for HF Spaces
+        server_port=7860,       # Standard port for HF Spaces
+        show_error=True
+    )