Update app.py

app.py

@@ -4,40 +4,41 @@ import gradio as gr
 from google import genai
 from google.genai import types
 
-# Correct imports for the MCP Python SDK 2026
+# Korrekte Import-Struktur für 2026
 from mcp import ClientSession
 from mcp.client.streamable_http import streamablehttp_client
 
 async def generate(input_text):
+    # MCP Server URL von Hugging Face
+    mcp_url = "https://mgokg-db-timetable-api.hf.space"
+    
     try:
-        # Initialize Gemini Client
+        # Gemini Client initialisieren
         client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
         
-        # MCP URL for Hugging Face
-        mcp_url = "https://mgokg-db-timetable-api.hf.space"
-        
-        # Using the streamablehttp_client transport
+        # Verbindung über den Streamable HTTP Transport
+        # Wir nutzen eine explizite Fehlerbehandlung für den Netzwerk-Stream
         async with streamablehttp_client(mcp_url) as (read_stream, write_stream):
             async with ClientSession(read_stream, write_stream) as mcp_session:
-                # Initialization of the protocol
+                # WICHTIG: Handshake initialisieren
                 await mcp_session.initialize()
-
-                # Gemini 2.0/2.5 Flash Model (2026 Standard)
-                model = "gemini-2.5-flash" 
                 
-                # Gemini can use the mcp_session directly as a tool
+                # Gemini Modellkonfiguration
+                # Gemini kann direkt auf mcp_session zugreifen, um Tools zu extrahieren
+                model_id = "gemini-2.0-flash" 
+                
                 generate_content_config = types.GenerateContentConfig(
                     temperature=0.4,
                     tools=[
                         types.Tool(google_search=types.GoogleSearch()),
-                        mcp_session 
+                        mcp_session # Übergabe der aktiven MCP-Sitzung
                     ],
-                    response_mime_type="text/plain",
                 )
 
                 response_text = ""
+                # Streaming der Antwort
                 async for chunk in client.aio.models.generate_content_stream(
-                    model=model,
+                    model=model_id,
                     contents=input_text,
                     config=generate_content_config,
                 ):
@@ -47,23 +48,21 @@ async def generate(input_text):
                 return response_text, ""
 
     except Exception as e:
-        return f"Error: {str(e)}", ""
+        # Detaillierte Fehlerausgabe für TaskGroup-Fehler
+        return f"Verbindungsfehler zum MCP-Server oder Gemini: {str(e)}", ""
 
 def gradio_wrapper(input_text):
+    # Führt die asynchrone Logik im synchronen Gradio-Kontext aus
     return asyncio.run(generate(input_text))
 
 if __name__ == '__main__':
     with gr.Blocks() as demo:
-        gr.Markdown("# Gemini Flash + DB Timetable (MCP)")
-        output_textbox = gr.Markdown()
-        input_textbox = gr.Textbox(lines=3, label="Query", 
-                                  placeholder="When is the next train from Berlin to Hamburg?")
-        submit_button = gr.Button("Submit")
+        gr.Markdown("# Gemini Flash + DB Timetable (Hugging Face MCP)")
+        with gr.Row():
+            input_tx = gr.Textbox(label="Anfrage", placeholder="Nächster Zug von Berlin nach Hamburg?")
+            btn = gr.Button("Senden")
+        output_md = gr.Markdown()
         
-        submit_button.click(
-            fn=gradio_wrapper,
-            inputs=input_textbox,
-            outputs=[output_textbox, input_textbox]
-        )
+        btn.click(fn=gradio_wrapper, inputs=input_tx, outputs=[output_md, input_tx])
         
-    demo.launch(show_error=True)
+    demo.launch()