Update app.py

app.py

@@ -15,59 +15,11 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-
-class RateLimitException(Exception):
-    pass
-
-@retry(
-    reraise=True,
-    stop=stop_after_attempt(10),  
-    wait=wait_exponential(multiplier=20, min=20, max=120),  
-    retry=retry_if_exception_type(RateLimitException),  
-)
-def call_openai_with_retry(**kwargs):
-    try:
-        return openai.ChatCompletion.create(**kwargs)
-    except openai.error.RateLimitError as e:
-        print("Rate limit error detected. Will retry...")
-        raise RateLimitException from e  
-    except Exception as e:
-        print(f"Non-rate-limit error occurred: {e}")
-        raise  
-
-class BasicAgent:
-    def __init__(self):
-        self.agent = CodeAgent(
-            model=OpenAIServerModel(model_id="gpt-4o"),
-            tools=[
-                DuckDuckGoSearchTool(),
-                WikipediaSearchTool()
-            ],
-            add_base_tools=True,
-        )
-        print("BasicAgent initialized.")
-        
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        try:
-            fixed_answer = call_openai_with_retry(
-                model="gpt-4o",
-                messages=[
-                    {"role": "user", "content": question}
-                ],
-                temperature=0.2,
-            )['choices'][0]['message']['content']
-        except Exception as e:
-            print(f"Agent error: {e}")
-            raise
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
-
-
-
+# --- Retry Helper for Agent Call ---
 def safe_agent_call(agent, question, retries=5, wait_time=20):
     """
-    Helper function to safely call the agent with retry on rate limit errors (HTTP 429).
+    Safely call the agent with retry on rate limit errors (HTTP 429).
+    Retries up to `retries` times, waits `wait_time` seconds between attempts.
     """
     for attempt in range(retries):
         try:
@@ -82,15 +34,37 @@ def safe_agent_call(agent, question, retries=5, wait_time=20):
                 raise e
     raise Exception(f"Failed after {retries} retries due to repeated rate limit errors.")
 
+# --- Basic Agent Definition ---
+class BasicAgent:
+    def __init__(self):
+        self.agent = CodeAgent(
+            model=OpenAIServerModel(model_id="gpt-4o"),
+            tools=[
+                DuckDuckGoSearchTool(),
+                WikipediaSearchTool(),
+            ],
+            add_base_tools=True,
+        )
+        print("✅ BasicAgent initialized.")
+
+    def __call__(self, question: str) -> str:
+        """
+        Calls the agent's run method to generate a response to the question.
+        """
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        fixed_answer = self.agent.run(question)
+        print(f"Agent returning answer: {fixed_answer}")
+        return fixed_answer
+
+# --- Main Logic for Fetching Questions, Running Agent, Submitting Answers ---
 def run_and_submit_all(profile: gr.OAuthProfile | None):
     """
-    Fetches all questions, runs the BasicAgent on them with retry logic on rate limit,
+    Fetches all questions, runs BasicAgent on them with retry logic on rate limit,
     submits all answers, and displays the results.
     """
 
-    # --- Determine HF Space Runtime URL and Repo URL ---
+    # Determine HF Space runtime info
     space_id = os.getenv("SPACE_ID")
-
     if profile:
         username = f"{profile.username}"
         print(f"User logged in: {username}")
@@ -98,23 +72,21 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
         print("User not logged in.")
         return "Please Login to Hugging Face with the button.", None
 
-    # --- Constants ---
     api_url = DEFAULT_API_URL
     questions_url = f"{api_url}/questions"
     submit_url = f"{api_url}/submit"
 
-    # 1. Instantiate Agent
+    # Instantiate Agent
     try:
         agent = BasicAgent()
     except Exception as e:
         print(f"Error instantiating agent: {e}")
         return f"Error initializing agent: {e}", None
 
-    # Build agent code link
     agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
+    print(f"Agent Code Repository: {agent_code}")
 
-    # 2. Fetch Questions
+    # Fetch Questions
     print(f"Fetching questions from: {questions_url}")
     try:
         response = requests.get(questions_url, timeout=15)
@@ -124,22 +96,14 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             print("Fetched questions list is empty.")
             return "Fetched questions list is empty or invalid format.", None
         print(f"Fetched {len(questions_data)} questions.")
-    except requests.exceptions.RequestException as e:
+    except Exception as e:
         print(f"Error fetching questions: {e}")
         return f"Error fetching questions: {e}", None
-    except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
-        return f"Error decoding server response for questions: {e}", None
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        return f"An unexpected error occurred fetching questions: {e}", None
 
-    # 3. Run your Agent
+    # Run Agent on Questions
     results_log = []
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
-
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
@@ -147,70 +111,54 @@ def run_and_submit_all(profile: gr.OAuthProfile | None):
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            # Using safe_agent_call to handle rate limit retries
             submitted_answer = safe_agent_call(agent, question_text)
-            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
+            answers_payload.append({
+                "task_id": task_id,
+                "submitted_answer": submitted_answer,
+            })
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer,
+            })
         except Exception as e:
             print(f"Error running agent on task {task_id}: {e}")
-            results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
+            results_log.append({
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": f"AGENT ERROR: {e}",
+            })
 
     if not answers_payload:
         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
+    # Prepare Submission
     submission_data = {
         "username": username.strip(),
         "agent_code": agent_code,
-        "answers": answers_payload
+        "answers": answers_payload,
     }
-    status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
-    print(status_update)
+    print(f"Submitting {len(answers_payload)} answers...")
 
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
+    # Submit Answers
     try:
         response = requests.post(submit_url, json=submission_data, timeout=60)
         response.raise_for_status()
         result_data = response.json()
         final_status = (
-            f"Submission Successful!\n"
+            f"✅ Submission Successful!\n"
             f"User: {result_data.get('username')}\n"
             f"Overall Score: {result_data.get('score', 'N/A')}% "
             f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
             f"Message: {result_data.get('message', 'No message received.')}"
         )
-        print("Submission successful.")
         results_df = pd.DataFrame(results_log)
         return final_status, results_df
-    except requests.exceptions.HTTPError as e:
-        error_detail = f"Server responded with status {e.response.status_code}."
-        try:
-            error_json = e.response.json()
-            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
-        except requests.exceptions.JSONDecodeError:
-            error_detail += f" Response: {e.response.text[:500]}"
-        status_message = f"Submission Failed: {error_detail}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.Timeout:
-        status_message = "Submission Failed: The request timed out."
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-    except requests.exceptions.RequestException as e:
-        status_message = f"Submission Failed: Network error - {e}"
-        print(status_message)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
     except Exception as e:
-        status_message = f"An unexpected error occurred during submission: {e}"
-        print(status_message)
+        print(f"Submission error: {e}")
         results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
+        return f"Submission Failed: {e}", results_df
 
 # --- Build Gradio Interface using Blocks ---
 with gr.Blocks() as demo: