Update app.py

app.py

@@ -146,81 +146,38 @@ def process_query(
     return final_state["result"]
 
 # Simple testing interface
-ui = gr.Interface(
-    fn=process_query,
-    inputs=gr.Textbox(lines=2, placeholder="Enter query here"),
-    outputs="text",
-    flagging_mode="never"
-)
-
-# Add a function to generate the graph visualization
-def get_graph_visualization():
-    """Generate and return the LangGraph workflow visualization as a PIL Image."""
-    # Generate the graph as PNG bytes
-    graph_png_bytes = graph.get_graph().draw_mermaid_png()
-    
-    # Convert bytes to PIL Image for Gradio display
-    graph_image = Image.open(io.BytesIO(graph_png_bytes))
-    return graph_image
 
 
 # Guidance for ChatUI - can be removed later. Questionable whether front end even necessary. Maybe nice to show the graph.
 with gr.Blocks(title="ChatFed Orchestrator") as demo:
-    gr.Markdown("# ChatFed Orchestrator")
-    gr.Markdown("This LangGraph server exposes MCP endpoints for the ChatUI module to call (which triggers the graph).")
     
     with gr.Row():
         # Left column - Graph visualization
-        with gr.Column(scale=1):
-            gr.Markdown("**Workflow Visualization**")
-            graph_display = gr.Image(
-                value=get_graph_visualization(), 
-                label="LangGraph Workflow",
-                interactive=False,
-                height=300
+        with gr.Column():
+            query_input = gr.Textbox(
+                label="query", 
+                lines=2, 
+                placeholder="Enter your search query here",
+                info="The query to search for in the vector database"
             )
+
+            submit_btn = gr.Button("Submit", variant="primary")
             
-            # Add a refresh button for the graph
-            refresh_graph_btn = gr.Button("🔄 Refresh Graph", size="sm")
-            refresh_graph_btn.click(
-                fn=get_graph_visualization,
-                outputs=graph_display
-            )
         
         # Right column - Interface and documentation  
-        with gr.Column(scale=2):
-            gr.Markdown("**Available MCP Tools:**")
-            
-            with gr.Accordion("MCP Endpoint Information", open=True):
-                gr.Markdown(f"""
-                **MCP Server Endpoint:** https://giz-chatfed-orchestrator.hf.space/gradio_api/mcp/sse
-                
-                **For ChatUI Integration:**
-                ```python
-                from gradio_client import Client
-                
-                # Connect to orchestrator
-                orchestrator_client = Client("https://giz-chatfed-orchestrator.hf.space")
-                
-                # Basic usage (no filters)
-                response = orchestrator_client.predict(
-                    query="query",
-                    api_name="/process_query"
-                )
-                
-                # Advanced usage with any combination of filters
-                response = orchestrator_client.predict(
-                    query="query",
-                    reports_filter="annual_reports",
-                    sources_filter="internal", 
-                    year_filter="2024",
-                    api_name="/process_query"
-                )
-                ```
-                """)
-
-    with gr.Accordion("Quick Testing Interface", open=True):
-        ui.render()
+        with gr.Column():
+            output = gr.Textbox(
+                label="answer",
+                lines=10,
+                show_copy_button=True
+            )
+
+    # UI event handler
+    submit_btn.click(
+        fn=retrieve,
+        inputs=[query_input, reports_input, sources_input, subtype_input, year_input],
+        outputs=output
+    )
 
 if __name__ == "__main__":
     demo.launch(