updated the underlying template following the multi tool MCP server from abidlabs

app.py

@@ -1,64 +1,75 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
+from textblob import TextBlob
+
+def sentiment_analysis(text: str) -> dict:
+    """
+    Analyze the sentiment of the given text.
+
+    Args:
+        text (str): The text to analyze
+
+    Returns:
+        dict: A dictionary containing polarity, subjectivity, and assessment
+    """
+    blob = TextBlob(text)
+    sentiment = blob.sentiment
+    
+    return {
+        "polarity": round(sentiment.polarity, 2),  # -1 (negative) to 1 (positive)
+        "subjectivity": round(sentiment.subjectivity, 2),  # 0 (objective) to 1 (subjective)
+        "assessment": "positive" if sentiment.polarity > 0 else "negative" if sentiment.polarity < 0 else "neutral"
+    }
+
+def driver_championship_score(driver_name: str) -> str:
+    """
+    Get the championship score for the given driver.
+
+    Args:
+        driver_name (str): The driver's name
+
+    Returns:
+        int: The driver's championship score
+    """
+    return f"Driver {driver_name} has {random.randint(0, 100)} championship points"
+
+
+def driver_position(driver_name: str) -> str:
+    """
+    Get the current position of the given driver.
+
+    Args:
+        driver_name (str): The driver's name
+
+    Returns:
+        str: The driver's current position
+    """
+    return f"Driver {driver_name} is in position {random.randint(1, 20)}"
+
+
+
+# Create interfaces for each tool
+iface1 = gr.Interface(
+    fn=driver_championship_score,
+    inputs="text",
+    outputs="text",
+    title="Driver Championship Score"
+)
+
+iface2 = gr.Interface(
+    fn=driver_position,
+    inputs="text",
+    outputs="text",
+    title="Driver Position"
 )
 
+# Combine into tabs into server
+gradio_server = gr.TabbedInterface(
+    [iface1, iface2],
+    tab_names=["Driver Championship Score", "Driver Position"],
+    title="Formula 1 MCP server",
+    description="Tools & Resources to query historical and real-time F1 facts and data."
+)
 
+# Launch the interface and MCP server
 if __name__ == "__main__":
-    demo.launch()
+    gradio_server.launch(mcp_server=True)