First Commit

.gitignore

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+.env

app.py

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+import os
+from fastapi import FastAPI
+from dotenv import load_dotenv
+from huggingface_hub.inference._mcp.agent import Agent
+import gradio as gr
+import uvicorn
+from fastapi.responses import RedirectResponse
+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","Qwen/Qwen1.5-0.5B-Chat")
+app=FastAPI(title="MODEL-CARD-CHATBOT")
+app.add_middleware(CORSMiddleware,allow_origins=["*"],allow_methods=["*"],allow_header=["*"])
+
+agent_instance: Optional[Agent]=None
+DEFAULT_PROVIDER:Literal['hf-inference']="hf-inference"
+async def get_agent():
+    """Get or create the agent instance."""
+    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"🤖 Model: {HF_MODEL}")
+        print(f"Provider: {DEFAULT_PROVIDER}")
+        try:
+            agent=Agent(model=HF_MODEL,provider="hf-inference",api_key=HF_TOKEN,
+                        servers=[{"type":"stdio","config":{
+                            "command":"python",
+                            "args":["mcp_server.py"],"cwd":".","env":{"HF_TOKEN": HF_TOKEN} if HF_TOKEN else {}}}])
+            print("🚀 Agent instance created successfully")
+            print("🔁 loading tools ...")
+            await agent_instance.load_tools()
+            print("✅ Tools loaded successfully")
+
+        except Exception as e:
+            print(f"❌ Error creating/loading agent: {str(e)}")
+    return agent_instance
+
+agent=get_agent()
+
+@app.on_event("startup")
+async def startup_event():
+    await agent.load_tools()
+
+def chat_function(user_message,history,model_id):
+
+    """Handles a user question by prompting the agent to read a model card and answer.
+    Args:
+        user_message (str): The user's question.
+        history (list): Chat history for display.
+        model_id (str): Hugging Face repo ID
+        
+    Returns:
+        Tuple[list,str]: Updated Chat history and empty string for clearing input."""
+    
+    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)]
+    try:
+        response=""
+        for output in agent.run(prompt):
+            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)
+    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")
+        with gr.Row():
+            model_id=gr.Textbox(label="MODEL ID", value="google/gemma-2-2b")
+            user_input=gr.Textbox(label="Your Question",value="Ask something about the model card .....")
+            chat=gr.Chatbot(label="chat")
+            send=gr.Button("Ask")
+            send.click(fn=chat_function,inputs=[user_input,chat,model_id],outputs=[chat,user_input])
+            return demo
+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)

mcp_server.py

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+from fastmcp import FastMCP
+from huggingface_hub import ModelCard
+from dotenv import load_dotenv
+load_dotenv()
+import os,json
+HF_TOKEN=os.getenv("HF_TOKEN")
+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."""
+    try:
+        card=ModelCard.load(repo_id)
+        return json.dumps({"status":"success","readme":str(card)})
+    
+    except FileNotFoundError:
+        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)})
+    
+
+    
+

requirements.txt

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+aiofiles==24.1.0
+annotated-types==0.7.0
+anyio==4.9.0
+attrs==25.3.0
+Authlib==1.6.1
+Brotli==1.1.0
+certifi==2025.7.14
+cffi==1.17.1
+charset-normalizer==3.4.2
+click==8.2.1
+colorama==0.4.6
+cryptography==45.0.5
+cyclopts==3.22.2
+dnspython==2.7.0
+docstring_parser==0.17.0
+docutils==0.21.2
+email_validator==2.2.0
+exceptiongroup==1.3.0
+fastapi==0.116.1
+fastmcp==2.10.6
+ffmpy==0.6.1
+filelock==3.18.0
+fsspec==2025.7.0
+gradio==5.38.0
+gradio_client==1.11.0
+groovy==0.1.2
+h11==0.16.0
+httpcore==1.0.9
+httpx==0.28.1
+httpx-sse==0.4.1
+huggingface-hub==0.33.4
+idna==3.10
+Jinja2==3.1.6
+jsonschema==4.25.0
+jsonschema-specifications==2025.4.1
+markdown-it-py==3.0.0
+MarkupSafe==3.0.2
+mcp==1.12.1
+mdurl==0.1.2
+numpy==2.3.1
+openapi-pydantic==0.5.1
+orjson==3.11.0
+packaging==25.0
+pandas==2.3.1
+pillow==11.3.0
+pycparser==2.22
+pydantic==2.11.7
+pydantic-settings==2.10.1
+pydantic_core==2.33.2
+pydub==0.25.1
+Pygments==2.19.2
+pyperclip==1.9.0
+python-dateutil==2.9.0.post0
+python-dotenv==1.1.1
+python-multipart==0.0.20
+pytz==2025.2
+pywin32==311
+PyYAML==6.0.2
+referencing==0.36.2
+requests==2.32.4
+rich==14.0.0
+rich-rst==1.3.1
+rpds-py==0.26.0
+ruff==0.12.4
+safehttpx==0.1.6
+semantic-version==2.10.0
+shellingham==1.5.4
+six==1.17.0
+sniffio==1.3.1
+sse-starlette==2.4.1
+starlette==0.47.2
+tomlkit==0.13.3
+tqdm==4.67.1
+typer==0.16.0
+typing-inspection==0.4.1
+typing_extensions==4.14.1
+tzdata==2025.2
+urllib3==2.5.0
+uvicorn==0.35.0
+websockets==15.0.1