Update app.py

app.py

@@ -11,16 +11,12 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
-openai_key = os.getenv("OPENAI_API_KEY")
-
 class BasicAgent:
-    def __init__(self, openai_key):
-        self.openai_key = openai_key
+    def __init__(self):
         print("BasicAgent initialized.")
         # Initialize the model
         #model = HfApiModel()
-        model = OpenAIServerModel(model_id="gpt-4.1", api_key=self.openai_key)
+        model = OpenAIServerModel(model_id="gpt-4o")
         # Initialize the search tool
         search_tool = DuckDuckGoSearchTool()
         # Initialize Agent
@@ -34,7 +30,7 @@ class BasicAgent:
         print(f"Agent returning fixed answer: {fixed_answer}")
         return fixed_answer
 
-def run_and_submit_all(profile: gr.OAuthProfile | None, openai_key: str):
+def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
     Fetches all questions, runs the BasicAgent on them, submits all answers,
     and displays the results.
@@ -55,7 +51,7 @@ def run_and_submit_all(profile: gr.OAuthProfile | None, openai_key: str):
 
     # 1. Instantiate Agent ( modify this part to create your agent)
     try:
-        agent = BasicAgent(openai_key)
+        agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
@@ -163,27 +159,25 @@ with gr.Blocks() as demo:
         **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.  Enter your OpenAI key below (if required by your agent).
-        4.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+        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).
-        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 separate action or even to answer the questions in async.
+        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.
+        Please note that this version requires an OpenAI Key to run.
         """
     )
 
     gr.LoginButton()
 
-    openai_key_box = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...", lines=1)
-
     run_button = gr.Button("Run Evaluation & Submit All Answers")
 
     status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
+    # Removed max_rows=10 from DataFrame constructor
     results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
 
     run_button.click(
         fn=run_and_submit_all,
-        inputs=[openai_key_box],
         outputs=[status_output, results_table]
     )
 
@@ -209,4 +203,4 @@ if __name__ == "__main__":
     print("-"*(60 + len(" App Starting ")) + "\n")
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)
+    demo.launch(debug=True, share=False)