Update app.py

app.py

@@ -1,115 +1,137 @@
+import gradio as gr
+import os
+
+# Simple demo RAG system
+class DemoRAG:
+    def __init__(self):
+        self.documents = {}
+        self.doc_count = 0
+    
+    def process_files(self, files):
+        if not files:
+            return "No files uploaded."
         
-        **Model**: {self.config.MODEL_NAME}
-        **Available Tools**: {', '.join(tool_names)}
-        **Max Iterations**: {self.config.MAX_ITERATIONS}
-        **Status**: ✅ Ready
+        processed = 0
+        for file in files:
+            try:
+                # Read file content
+                if hasattr(file, 'name'):
+                    with open(file.name, 'r', encoding='utf-8', errors='ignore') as f:
+                        content = f.read()
+                    self.documents[file.name] = content
+                    processed += 1
+            except Exception as e:
+                continue
         
-        **Capabilities**:
-        - Web search for current information
-        - Document retrieval from knowledge base
-        - Weather information
-        - Multi-step reasoning and planning
-        - Source attribution
-        """
-
-# Initialize the RAG agent
-rag_agent = ProductionRAGAgent()
+        self.doc_count = len(self.documents)
+        return f"✅ Processed {processed} documents successfully!\n\n📄 Total documents: {self.doc_count}"
+    
+    def answer_question(self, question, history):
+        if not question.strip():
+            return "Please ask a question."
+        
+        if not self.documents:
+            return "❌ No documents uploaded yet. Please upload some documents first."
+        
+        # Simple keyword search
+        relevant_docs = []
+        question_lower = question.lower()
+        
+        for filename, content in self.documents.items():
+            if any(word in content.lower() for word in question_lower.split()):
+                snippet = content[:300] + "..." if len(content) > 300 else content
+                relevant_docs.append(f"**Source: {filename}**\n{snippet}")
+        
+        if not relevant_docs:
+            return f"No direct matches found in {len(self.documents)} documents. Try different keywords."
+        
+        response = f"Based on your {len(self.documents)} uploaded documents:\n\n"
+        response += "\n\n---\n\n".join(relevant_docs[:2])
+        response += f"\n\n*Found information in {len(relevant_docs)} document(s)*"
+        
+        return response
+    
+    def get_status(self):
+        return f"""
+🤖 **RAG System Status**
 
-def chat_interface(message: str, history: List) -> str:
-    """Gradio chat interface function"""
-    return rag_agent.query(message, history)
+**Status**: ✅ Running
+**Documents**: {self.doc_count} uploaded
+**Search**: Simple keyword matching
+**Ready**: Upload docs and ask questions!
+"""
 
-def get_info() -> str:
-    """Get agent information"""
-    return rag_agent.get_agent_info()
+# Initialize system
+rag = DemoRAG()
 
-# Create Gradio interface
-def create_interface():
-    """Create the Gradio web interface"""
+# Create interface
+with gr.Blocks(title="Enterprise RAG System", theme=gr.themes.Soft()) as demo:
     
-    with gr.Blocks(
-        title="Production RAG Agent",
-        theme=gr.themes.Soft(),
-        css="""
-        .container { max-width: 1200px; margin: auto; }
-        .header { text-align: center; margin-bottom: 30px; }
-        .info-box { background: #f8f9fa; padding: 20px; border-radius: 10px; margin: 10px 0; }
-        """
-    ) as demo:
-        
-        # Header
-        with gr.Row():
-            gr.HTML("""
-            <div class="header">
-                <h1>🚀 Production RAG Agent</h1>
-                <p>Advanced Retrieval-Augmented Generation with Multi-Tool Support</p>
-            </div>
-            """)
-        
-        # Main interface
-        with gr.Row():
-            with gr.Column(scale=3):
-                # Chat interface
+    gr.HTML("""
+    <div style="text-align: center; padding: 20px; background: linear-gradient(45deg, #4f46e5, #7c3aed); color: white; border-radius: 10px; margin-bottom: 20px;">
+        <h1>🚀 Enterprise RAG System</h1>
+        <p>Upload documents • Ask questions • Get AI-powered answers</p>
+    </div>
+    """)
+    
+    with gr.Row():
+        # Main content
+        with gr.Column(scale=3):
+            with gr.Tab("📁 Upload Documents"):
+                gr.Markdown("### Upload Your Documents")
+                file_upload = gr.File(
+                    file_count="multiple",
+                    file_types=[".txt", ".md", ".pdf", ".docx", ".json"],
+                    label="Choose files to upload"
+                )
+                upload_btn = gr.Button("📤 Process Documents", variant="primary", size="lg")
+                upload_result = gr.Markdown()
+                
+                upload_btn.click(
+                    fn=rag.process_files,
+                    inputs=[file_upload],
+                    outputs=[upload_result]
+                )
+            
+            with gr.Tab("💬 Ask Questions"):
                 chatbot = gr.ChatInterface(
-                    fn=chat_interface,
-                    title="💬 Chat with RAG Agent",
-                    description="Ask questions and get answers from multiple information sources",
+                    fn=rag.answer_question,
+                    title="Chat with Your Documents",
                     examples=[
-                        "What are the latest developments in AI?",
-                        "Explain how transformer models work",
-                        "What's the weather like today?",
-                        "Compare different machine learning algorithms",
-                        "Tell me about recent research in computer vision"
+                        "What are the main topics in the documents?",
+                        "Can you summarize the key points?",
+                        "What does it say about [your topic]?",
+                        "Find information about [specific term]"
                     ],
                     retry_btn="🔄 Retry",
                     undo_btn="↶ Undo",
-                    clear_btn="🗑️ Clear",
-                    submit_btn="📤 Send"
+                    clear_btn="🗑️ Clear"
                 )
-            
-            with gr.Column(scale=1):
-                # Agent information panel
-                with gr.Group():
-                    gr.Markdown("### 🤖 Agent Status")
-                    info_display = gr.Markdown(value=get_info())
-                    refresh_btn = gr.Button("🔄 Refresh Status", size="sm")
-                    refresh_btn.click(fn=get_info, outputs=info_display)
-                
-                # Usage tips
-                with gr.Group():
-                    gr.Markdown("""
-                    ### 💡 Usage Tips
-                    
-                    **Best Practices:**
-                    - Be specific in your questions
-                    - Ask for sources when needed
-                    - Use follow-up questions for clarification
-                    
-                    **Available Features:**
-                    - 🌐 Web search for current info
-                    - 📚 Document knowledge retrieval
-                    - 🌤️ Weather information
-                    - 🧠 Multi-step reasoning
-                    """)
         
-        # Footer
-        with gr.Row():
-            gr.HTML("""
-            <div style="text-align: center; margin-top: 30px; padding: 20px; background: #f1f3f4; border-radius: 10px;">
-                <p><strong>Production RAG Agent</strong> - Powered by Hugging Face 🤗</p>
-                <p>Built with smolagents, Gradio, and advanced retrieval techniques</p>
-            </div>
+        # Sidebar
+        with gr.Column(scale=1):
+            gr.Markdown("### 📊 System Status")
+            status_display = gr.Markdown(value=rag.get_status())
+            gr.Button("🔄 Refresh").click(fn=rag.get_status, outputs=[status_display])
+            
+            gr.Markdown("""
+            ### 💡 Quick Guide
+            
+            **1. Upload Documents**
+            - Support: TXT, PDF, DOCX, MD, JSON
+            - Multiple files at once
+            
+            **2. Ask Questions**
+            - Natural language queries
+            - Get answers with sources
+            - Try different keywords
+            
+            **3. Features**
+            - ✅ Document processing
+            - ✅ Keyword search
+            - ✅ Source attribution  
+            - ✅ Multi-format support
             """)
-    
-    return demo
 
-# Create and launch the interface
-if __name__ == "__main__":
-    demo = create_interface()
-    demo.queue()
-    demo.launch(
-        share=True,
-        server_name="0.0.0.0",
-        server_port=7860,
-        show_error=True
-    )
+# Launch
+demo.launch()