Update app.py

app.py

@@ -1,36 +1,37 @@
 # 17/08/2025 -AO
-
 import os
 import gradio as gr
 import requests
+import inspect
 import pandas as pd
-from transformers import HfAgent
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, HfAgent
 from transformers.tools import DuckDuckGoSearchTool
+from huggingface_hub import InferenceClient
 
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Advanced Agent Definition ---
+# ----- THIS IS WHERE YOU BUILD YOUR AGENT ------
 class MyAgent:
     def __init__(self):
-        # Define the LLM for the agent. Use a supported model from the Hugging Face Hub.
-        # Ensure you have the necessary environment variables (e.g., HF_TOKEN).
-        # You'll likely need a powerful model to get a high score.
-        # Example: Using a powerful hosted model
-        self.agent = HfAgent("HuggingFaceH4/zephyr-7b-beta",
-                             additional_tools=[DuckDuckGoSearchTool()])
+        # Initialize the model and tokenizer for tool use
+        print("Initializing MyAgent with Qwen2.5-72B-Instruct...")
+      
+        # Muista asettaa -> HF_TOKEN secret in your space settings.
+        self.agent = HfAgent(
+            llm="Qwen/Qwen2.5-72B-Instruct",
+            additional_tools=[DuckDuckGoSearchTool()],
+        )
+        print("Agent initialized with InferenceClient.")
 
     def __call__(self, question: str) -> str:
         print(f"Agent received question: {question[:50]}...")
-        # Use a try-except block to handle potential errors during execution.
         try:
-            # Let the agent reason and find the answer.
-            # Use verbose=True to see the agent's thought process for debugging.
             result = self.agent.run(question)
+            
             # The GAIA scoring is an "EXACT MATCH".
-            # The prompt asks the agent to just return the answer, not a verbose explanation.
-            # You might need to add a post-processing step to extract the final answer.
-            # For example, by telling the model in the prompt to only output the answer.
             final_answer = str(result)
             print(f"Agent returning answer: {final_answer}")
             return final_answer
@@ -40,14 +41,11 @@ class MyAgent:
 
 # The rest of the `run_and_submit_all` and Gradio code remains the same as in the template.
 def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the agent on them, submits all answers,
-    and displays the results.
-    """
+    # --- Determine HF Space Runtime URL and Repo URL ---
     space_id = os.getenv("SPACE_ID")
 
     if profile:
-        username = f"{profile.username}"
+        username= f"{profile.username}"
         print(f"User logged in: {username}")
     else:
         print("User not logged in.")
@@ -57,13 +55,13 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
+    # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = MyAgent()  # Instantiate your new agent class
+        agent = MyAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
-    
+    # In the case of an app running as a hugging Face space, this link points toward your codebase ( usefull for others so please keep it public)
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
     print(agent_code)
 
@@ -110,7 +108,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         print("Agent did not produce any answers to submit.")
         return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
-    # 4. Prepare Submission 
+    # 4. Prepare Submission
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)
@@ -158,10 +156,9 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         results_df = pd.DataFrame(results_log)
         return status_message, results_df
 
-
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo:
-    gr.Markdown("# Advanced Agent Evaluation Runner")
+    gr.Markdown("# Basic Agent Evaluation Runner")
     gr.Markdown(
         """
         **Instructions:**
@@ -171,6 +168,7 @@ with gr.Blocks() as demo:
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
         """
     )
+
     gr.LoginButton()
 
     run_button = gr.Button("Run Evaluation & Submit All Answers")
@@ -185,8 +183,9 @@ with gr.Blocks() as demo:
 
 if __name__ == "__main__":
     print("\n" + "-"*30 + " App Starting " + "-"*30)
+    # Check for SPACE_HOST and SPACE_ID at startup for information
     space_host_startup = os.getenv("SPACE_HOST")
-    space_id_startup = os.getenv("SPACE_ID")
+    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
 
     if space_host_startup:
         print(f"✅ SPACE_HOST found: {space_host_startup}")
@@ -194,7 +193,7 @@ if __name__ == "__main__":
     else:
         print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
 
-    if space_id_startup:
+    if space_id_startup: # Print repo URLs if SPACE_ID is found
         print(f"✅ SPACE_ID found: {space_id_startup}")
         print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
         print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
@@ -203,5 +202,5 @@ if __name__ == "__main__":
 
     print("-"*(60 + len(" App Starting ")) + "\n")
 
-    print("Launching Gradio Interface for Advanced Agent Evaluation...")
+    print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)