Update app.py

app.py

@@ -3,30 +3,67 @@ import gradio as gr
 import requests
 import inspect
 import pandas as pd
-from agents import create_agent_flow
-from langchain_core.messages import HumanMessage
+import time
+from smolagents import CodeAgent, DuckDuckGoSearchTool, WikipediaSearchTool, PythonInterpreterTool, FinalAnswerTool, LiteLLMModel
+
 
 # (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:
+
     
+# Define the real agent
+class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
-        self.agent = create_agent_flow()
-    
+        print("Improved BasicAgent initialized with Gemini and enhanced tools.")
+
+        # Load Gemini through LiteLLM
+        self.model = LiteLLMModel(
+            model_id="gemini/gemini-2.0-flash-lite",
+            api_key=os.getenv("GEMINI_API_TOKEN"),
+        )
+
+        # Setup CodeAgent with tools
+        self.agent = CodeAgent(
+            tools=[
+                DuckDuckGoSearchTool(),
+                WikipediaSearchTool(),
+                PythonInterpreterTool(),
+                FinalAnswerTool(),
+            ],
+            model=self.model,
+            max_steps=10,
+            add_base_tools=True,
+            additional_authorized_imports=["pandas", "*"]
+        )
+
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-      
-        question = [HumanMessage(content=question)]
-        question_ask = self.agent.invoke({"messages": question})
-        response = question_ask['messages'][-1].content
-        print(f"Agent returning fixed answer: {response}")
-        return response[8:]
+        """Main callable for processing questions with rate limiting."""
+        print(f"Running agent for task: {question[:50]}...")
+
+        # Prompt with strict output formatting
+        system_instruction = (
+            "I will ask you a question. Report your thoughts step by step. "
+            "Finish your answer only with the final answer. In the final answer don't write explanations. "
+            "The answer should be a number OR as few words as possible OR a comma separated list of numbers and/or strings. "
+            "Avoid units, abbreviations, or articles unless specified. "
+            "Pay attention to each sentence in the question and verify the answer against every part. "
+            "Try searching more sources if initial results are insufficient.\nQUESTION: "
+        )
+
+        prompt = system_instruction + question
+        try:
+            result = self.agent.run(prompt)
+        except Exception as e:
+            result = f"Error: {str(e)}"
+
+        # Rate limiting to avoid 429 errors or API limits
+        print("Waiting 5 seconds to respect rate limits...")
+        time.sleep(5)
 
+        return result
+            
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
@@ -154,9 +191,11 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
+
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         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.
+
         ---
         **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).