test

app.py

@@ -1,13 +1,17 @@
 import inspect
 import os
 
+from typing import Any, Dict, List, Optional, TypedDict
+
 import gradio as gr
 import pandas as pd
 import requests
+from langchain_core.messages import HumanMessage
+from langchain_openai import ChatOpenAI
+from langgraph.graph import END, START, StateGraph
 
-from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 
-hf_token = os.getenv("HF_TOKEN")
+hf_token = os.getenv("OPENAI_API_KEY")
 
 
 # (Keep Constants as is)
@@ -19,20 +23,24 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        self.llm = HuggingFaceInferenceAPI(
-            model_name="deepseek-ai/DeepSeek-R1",
-            temperature=0.7,
-            max_tokens=100,
-            token=hf_token,
-            provider="nebius",
-        )
+        self.model = ChatOpenAI(model="gpt-4o", temperature=0)
 
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        response = self.llm.complete("Hello, how are you?")
-
-        print(f"Agent returning  answer: {response}")
-        return response.text
+        prompt = f"""
+You are a general AI assistant. I will ask you a question.
+Report your thoughts, and finish your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
+YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated list of numbers and/or strings.
+If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise.
+If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise.
+If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string.
+Question: {question}
+            """
+        messages = [HumanMessage(content=prompt)]
+        response = self.model.invoke(messages)
+
+        print(f"Agent returning answer: {response.text()}")
+        return response.text()
 
 
 def run_and_submit_all(profile: gr.OAuthProfile | None):
@@ -91,7 +99,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-    for item in questions_data:
+    for item in questions_data[:1]:
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None: