Use a simple LLM agent

app.py

@@ -1,23 +1,31 @@
 import os
 import gradio as gr
 import requests
-import inspect
 import pandas as pd
 
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
-# --- Basic Agent Definition ---
-# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-class BasicAgent:
+
+llm = HuggingFaceInferenceAPI(model_name='Qwen/Qwen2.5-Coder-32B-Instruct')
+
+class TofuAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
+        print("Agent 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
+        try:
+            # Use the llm to generate an answer
+            response = llm.generate(question)
+            print(f"Agent generated response: {response}")
+            return response
+        except Exception as e:
+            print(f"Error generating response: {e}")
+            return f"Error: {e}"
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -40,7 +48,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent()
+        agent = TofuAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None

requirements.txt

@@ -1,2 +1,3 @@
 gradio
-requests
+requests
+pandas