Update app.py

app.py

@@ -4,28 +4,28 @@ import gradio as gr
 from google import genai
 from google.genai import types
 
-# Korrekte Importe für das MCP Python SDK 2026
+# Correct imports for the MCP Python SDK 2026
 from mcp import ClientSession
-from mcp.client.http import HttpClientTransport
+from mcp.client.streamable_http import streamablehttp_client
 
 async def generate(input_text):
     try:
-        # Gemini Client initialisieren
+        # Initialize Gemini Client
         client = genai.Client(api_key=os.environ.get("GEMINI_API_KEY"))
         
-        # MCP Konfiguration für Hugging Face (Streamable HTTP)
+        # MCP URL for Hugging Face
         mcp_url = "https://mgokg-db-timetable-api.hf.space"
         
-        # 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
+        # Using the streamablehttp_client transport
+        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
                 await mcp_session.initialize()
 
-                model = "gemini-2.0-flash" 
+                # Gemini 2.0/2.5 Flash Model (2026 Standard)
+                model = "gemini-2.5-flash" 
                 
-                # Tools definieren: Google Search + die Session des MCP Servers
-                # Das SDK erkennt die Tools automatisch aus der mcp_session
+                # Gemini can use the mcp_session directly as a tool
                 generate_content_config = types.GenerateContentConfig(
                     temperature=0.4,
                     tools=[
@@ -36,7 +36,6 @@ async def generate(input_text):
                 )
 
                 response_text = ""
-                # Streaming-Abfrage an Gemini
                 async for chunk in client.aio.models.generate_content_stream(
                     model=model,
                     contents=input_text,
@@ -48,19 +47,18 @@ async def generate(input_text):
                 return response_text, ""
 
     except Exception as e:
-        return f"Fehler: {str(e)}", ""
+        return f"Error: {str(e)}", ""
 
-# 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 (Hugging Face MCP)")
+        gr.Markdown("# Gemini Flash + DB Timetable (MCP)")
         output_textbox = gr.Markdown()
-        input_textbox = gr.Textbox(lines=3, label="Anfrage", 
-                                  placeholder="Wann fährt der nächste Zug von München nach Hamburg?")
-        submit_button = gr.Button("Anfrage senden")
+        input_textbox = gr.Textbox(lines=3, label="Query", 
+                                  placeholder="When is the next train from Berlin to Hamburg?")
+        submit_button = gr.Button("Submit")
         
         submit_button.click(
             fn=gradio_wrapper,