Refactor app.py to implement SmolAgent with Hugging Face integration, replacing BasicAgent.

app.py

@@ -3,21 +3,53 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
+from dotenv import load_dotenv
+from smolagents import CodeAgent, InferenceClientModel
+
+load_dotenv()
 
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+HF_TOKEN = os.getenv("HF_TOKEN")
 
-# --- Basic Agent Definition ---
+# --- Smol Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
+class SmolAgent:
+    def __init__(self, hf_token: str):
+        print("Initializing SmolAgent...")
+        if not hf_token:
+            raise ValueError("Hugging Face token not found. Please set HF_TOKEN environment variable.")
+        
+        # 3. Initialize the SmolLM model
+        model = InferenceClientModel(
+            model_id="HuggingFaceTB/SmolLM-360M-Instruct",
+            token=hf_token,
+        )
+        
+        # 4. Replace your current BasicAgent with a smolagents.CodeAgent
+        self._agent = CodeAgent(
+            tools=[],
+            model=model,
+        )
+        print("SmolAgent initialized.")
+
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        # 6. Prompt carefully - optimized for evaluation tasks
+        prompt = f"""Answer the following question with a short, direct response. Be concise and accurate:
+
+Question: {question}
+
+Answer:"""
+        try:
+            answer = self._agent.run(prompt)
+            print(f"Agent returning answer: {answer}")
+            return str(answer)
+        except Exception as e:
+            print(f"Error running agent: {e}")
+            return f"AGENT ERROR: {e}"
+
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +72,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = SmolAgent(hf_token=HF_TOKEN)
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -142,16 +174,21 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
+    gr.Markdown("# SmolLM Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
 
-        1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
+        1.  This space uses SmolLM-360M-Instruct model with smolagents for question answering.
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
 
         ---
+        **Model Information:**
+        - Using: HuggingFaceTB/SmolLM-360M-Instruct
+        - Framework: smolagents CodeAgent
+        - No additional tools (pure reasoning)
+
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).
         This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.