first commit

app.py

@@ -1,3 +1,4 @@
+# app.py
 import os
 from fastapi import FastAPI
 from dotenv import load_dotenv
@@ -9,26 +10,27 @@ from fastapi.middleware.cors import CORSMiddleware
 from typing import Optional, Literal
 
 load_dotenv()
-HF_TOKEN=os.getenv("HF_TOKEN")
-HF_MODEL=os.getenv("HF_MODEL","google/gemma-2-2b")
-app=FastAPI(title="MODEL-CARD-CHATBOT")
-app.add_middleware(CORSMiddleware, allow_origins=["*"], allow_methods=['*'], allow_headers=['*'])
+HF_TOKEN = os.getenv("HF_TOKEN")
+HF_MODEL = os.getenv("HF_MODEL", "google/gemma-2-2b")
 
-agent_instance: Optional[Agent]=None
-DEFAULT_PROVIDER:Literal['hf-inference']="hf-inference"
+app = FastAPI(title="Model Card Chatbot")
+app.add_middleware(
+    CORSMiddleware, allow_origins=["*"], allow_methods=["*"], allow_headers=["*"]
+)
 
+agent_instance: Optional[Agent] = None
+DEFAULT_PROVIDER: Literal['hf-inference'] = "hf-inference"
 
 async def get_agent():
     global agent_instance
     if agent_instance is None and HF_TOKEN:
         print("🔧 Creating new Agent instance ...")
-        print(f"✅ HF_TOKEN present : {bool(HF_TOKEN)}")
+        print(f"✅ HF_TOKEN present: {bool(HF_TOKEN)}")
         print(f"🤖 Model: {HF_MODEL}")
-        print(f"Provider: {DEFAULT_PROVIDER}")
         try:
             agent = Agent(
                 model=HF_MODEL,
-                provider="hf-inference",
+                provider=DEFAULT_PROVIDER,
                 api_key=HF_TOKEN,
                 servers=[{
                     "type": "stdio",
@@ -52,42 +54,54 @@ async def startup_event():
     global agent_instance
     agent_instance = await get_agent()
 
-
-
 def chat_function(user_message, history, model_id):
     global agent_instance
-    prompt=f"""You're an assistant helping with hugging face model cards.
-        First, run the tool `read_model_card` on repo_id `{model_id}` to get the model card.
-        Then answer this user question based on the model card:
-        User question: {user_message}"""
+    prompt = f"""
+You're an assistant helping with Hugging Face model cards.
+First, run the tool `read_model_card` on repo_id `{model_id}` to get the model card.
+Then answer this user question based on the model card:
+User question: {user_message}
+"""
     history = history + [(user_message, None)]
     try:
         response = ""
-        for output in agent_instance.run(prompt):
+        outputs = agent_instance.run(prompt)
+        for output in outputs:
             if hasattr(output, "content") and output.content:
                 response = output.content
-
-        final_response = response or "⚠️ Sorry, I couldn't generate a response."
-        history[-1] = (user_message, final_response)
+        if not response:
+            response = "⚠️ Sorry, I couldn't generate a response."
+        history[-1] = (user_message, response)
     except Exception as e:
         history[-1] = (user_message, f"⚠️ Error: {str(e)}")
     return history, ""
 
-
 def create_gradio_app():
-    with gr.Blocks(title="Model Card Chatbot") as demo:
-        gr.Markdown("## 🤖 Model Card Chatbot\nAsk questions about Hugging Face model card")
-        model_id=gr.Textbox(label="MODEL ID", value="google/gemma-2-2b")
-        user_input=gr.Textbox(label="Your Question",placeholder="Ask something about the model card")
-        send=gr.Button("Ask")
-        chatbot=gr.Chatbot(label="chat")
-        send.click(fn=chat_function, inputs=[user_input,chatbot,model_id], outputs=[chatbot,user_input])
+    with gr.Blocks(theme=gr.themes.Soft(), title="🤖 Model Card Chatbot") as demo:
+        gr.Markdown("""
+        # 🤖 **Model Card Chatbot**
+        Ask anything about a model's card on Hugging Face.
+        """)
+        with gr.Row():
+            model_id = gr.Textbox(label="Model ID", value="google/gemma-2-2b", scale=2)
+            user_input = gr.Textbox(label="Your Question", placeholder="e.g., What is this model trained on?", scale=3)
+            send = gr.Button("🔍 Ask", scale=1)
+        chatbot = gr.Chatbot(label="Chat")
+        
+        send.click(
+            fn=chat_function,
+            inputs=[user_input, chatbot, model_id],
+            outputs=[chatbot, user_input]
+        )
     return demo
-gradio_app=create_gradio_app()
-app=gr.mount_gradio_app(app,gradio_app,path="/")
+
+gradio_app = create_gradio_app()
+app = gr.mount_gradio_app(app, gradio_app, path="/")
 
 @app.get("/")
 async def root():
     return RedirectResponse("/")
-if __name__=="__main__":
-    uvicorn.run("app:app",host="0.0.0.0",port=7860,reload=True)
+
+if __name__ == "__main__":
+    uvicorn.run("app:app", host="0.0.0.0", port=7860, reload=True)
+

mcp_server.py

@@ -1,23 +1,25 @@
+# mcp_server.py
 from fastmcp import FastMCP
 from huggingface_hub import ModelCard
+import json
 from dotenv import load_dotenv
 load_dotenv()
-import os,json
-HF_TOKEN=os.getenv("HF_TOKEN")
-mcp=FastMCP("model_card-chatbot")
 
+mcp = FastMCP("model_card-chatbot")
 
 @mcp.tool()
-def read_model_card(repo_id:str)->str:
-    """Tool to read and return the full model card from hugging face."""
+def read_model_card(repo_id: str) -> str:
+    """
+    Tool to read and return the full model card from Hugging Face.
+    """
     try:
-        card=ModelCard.load(repo_id)
-        return json.dumps({"status":"success","readme": card.text})
-    
+        card = ModelCard.load(repo_id)
+        return json.dumps({"status": "success", "readme": card.text})
     except FileNotFoundError:
-        return json.dumps({"status":"error","message":"This model does not have a model card"})
+        return json.dumps({"status": "error", "message": "This model does not have a model card"})
     except Exception as e:
-        return json.dumps({"status":"error","message":str(e)})
+        return json.dumps({"status": "error", "message": str(e)})
+