Update app.py

app.py

@@ -3,62 +3,64 @@ import asyncio
 import gradio as gr
 from google import genai
 from google.genai import types
-# Import MCP client for Python (standard in 2026)
-from mcp import Client as MCPClient
+
+# Korrekte Importe für das MCP Python SDK 2026
+from mcp import ClientSession
+from mcp.client.http import HttpClientTransport
 
 async def generate(input_text):
     try:
-        # Initialize Gemini Client
-        client = genai.Client(
-            api_key=os.environ.get("GEMINI_API_KEY"),
-        )
+        # Gemini Client initialisieren
+        client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
         
-        # 1. Connect to the Streamable HTTP MCP server on Hugging Face
-        # 2026 SDKs support direct URL initialization
-        mcp_url = "https://mgokg-db-timetable-api.hf.space/gradio_api/mcp/"
+        # MCP Konfiguration für Hugging Face (Streamable HTTP)
+        mcp_url = "https://mgokg-db-timetable-api.hf.space"
         
-        async with MCPClient(url=mcp_url) as mcp_session:
-            
-            model = "gemini-2.5-flash" # Version 2026
-            
-            # 2. Define tools: Google Search + MCP Session
-            tools = [
-                types.Tool(google_search=types.GoogleSearch()),
-                mcp_session # Pass the entire MCP server as a toolset here
-            ]
-            
-            generate_content_config = types.GenerateContentConfig(
-                temperature=0.4,
-                tools=tools,
-                response_mime_type="text/plain",
-            )
+        # Verbindung über HTTP Transport herstellen
+        async with HttpClientTransport(mcp_url) as transport:
+            async with ClientSession(transport.read, transport.write) as mcp_session:
+                # Initialisierung des MCP-Protokolls
+                await mcp_session.initialize()
+
+                model = "gemini-2.0-flash" 
+                
+                # Tools definieren: Google Search + die Session des MCP Servers
+                # Das SDK erkennt die Tools automatisch aus der mcp_session
+                generate_content_config = types.GenerateContentConfig(
+                    temperature=0.4,
+                    tools=[
+                        types.Tool(google_search=types.GoogleSearch()),
+                        mcp_session 
+                    ],
+                    response_mime_type="text/plain",
+                )
 
-            response_text = ""
-            # 3. Stream call with MCP tools
-            async for chunk in client.aio.models.generate_content_stream(
-                model=model,
-                contents=input_text,
-                config=generate_content_config,
-            ):
-                if chunk.text:
-                    response_text += chunk.text
-            
-            return response_text, ""
+                response_text = ""
+                # Streaming-Abfrage an Gemini
+                async for chunk in client.aio.models.generate_content_stream(
+                    model=model,
+                    contents=input_text,
+                    config=generate_content_config,
+                ):
+                    if chunk.text:
+                        response_text += chunk.text
+                
+                return response_text, ""
 
     except Exception as e:
-        return f"Error during processing: {str(e)}", ""
+        return f"Fehler: {str(e)}", ""
 
-# Helper function for asynchronous Gradio calls
+# Hilfsfunktion für den asynchronen Aufruf in Gradio
 def gradio_wrapper(input_text):
     return asyncio.run(generate(input_text))
 
 if __name__ == '__main__':
     with gr.Blocks() as demo:
-        gr.Markdown("# Gemini Flash + DB Timetable (MCP)")
+        gr.Markdown("# Gemini Flash + DB Timetable (Hugging Face MCP)")
         output_textbox = gr.Markdown()
-        input_textbox = gr.Textbox(lines=3, label="Request",
-                                  placeholder="When is the next train from Berlin to Hamburg?")
-        submit_button = gr.Button("Send Request")
+        input_textbox = gr.Textbox(lines=3, label="Anfrage", 
+                                  placeholder="Wann fährt der nächste Zug von München nach Hamburg?")
+        submit_button = gr.Button("Anfrage senden")
         
         submit_button.click(
             fn=gradio_wrapper,