Update logic.py

logic.py

@@ -1,207 +1,210 @@
-from typing import Dict, List, Tuple
-import re
-import tempfile
-from pathlib import Path
-import pandas as pd
-import requests
-from agent import GaiaAgent
-from pandas import DataFrame
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
-SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
-FILE_PATH = f"{DEFAULT_API_URL}/files/"
-
-
-# --- Helper Methods ---
-def fetch_all_questions() -> Dict:
-    """Fetches all questions from the specified API endpoint.
-
-    This function retrieves a list of questions from the API, handles potential errors
-    such as network issues, invalid responses, or empty question lists, and returns
-    the questions as a dictionary.
-
-    Returns:
-        Dict: A dictionary containing the questions data retrieved from the API.
-
-    Raises:
-        UserWarning: If there is an error fetching the questions, such as network issues,
-            invalid JSON response, or an empty question list.  The exception message
-            provides details about the specific error encountered.
-    """
-    print(f"Fetching questions from: {QUESTIONS_URL}")
-    response = requests.get(QUESTIONS_URL, timeout=15)
-    try:
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            print("Fetched questions list is empty.")
-            raise UserWarning("Fetched questions list is empty or invalid format.")
-        print(f"Fetched {len(questions_data)} questions.")
-        return questions_data
-    except requests.exceptions.RequestException as e:
-        print(f"Error fetching questions: {e}")
-        raise UserWarning(f"Error fetching questions: {e}")
-    except requests.exceptions.JSONDecodeError as e:
-        print(f"Error decoding JSON response from questions endpoint: {e}")
-        print(f"Response text: {response.text[:500]}")
-        raise UserWarning(f"Error decoding server response for questions: {e}")
-    except Exception as e:
-        print(f"An unexpected error occurred fetching questions: {e}")
-        raise UserWarning(f"An unexpected error occurred fetching questions: {e}")
-
-
-def submit_answers(submission_data: dict, results_log: list) -> Tuple[str, DataFrame]:
-    """Submits answers to the scoring API and returns the submission status and results.
-
-    This function sends the provided answers to the scoring API, handles potential errors
-    such as network issues, server errors, or invalid responses, and returns a status
-    message indicating the success or failure of the submission, along with a DataFrame
-    containing the results log.
-
-    Args:
-        submission_data (dict): A dictionary containing the answers to be submitted.
-            Expected to have a structure compatible with the scoring API.
-        results_log (list): A list of dictionaries containing the results log.
-            This log is converted to a Pandas DataFrame and returned.
-
-    Returns:
-        Tuple[str, DataFrame]: A tuple containing:
-            - A status message (str) indicating the submission status and any relevant
-              information or error messages.
-            - A Pandas DataFrame containing the results log.
-
-    """
-    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"User: {result_data.get('username')}\n"
-            f"Overall Score: {result_data.get('score', 'N/A')}% "
-            f"({result_data.get('correct_count', '?')}/"
-            f"{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)
-        results_df = pd.DataFrame(results_log)
-        return status_message, results_df
-
-
-def run_agent(
-    gaia_agent: GaiaAgent, questions_data: List[Dict]
-) -> Tuple[List[Dict], List[Dict]]:
-    """Runs the agent on a list of questions and returns the results and answers.
-
-    This function iterates through a list of questions, runs the provided agent on each
-    question, and collects the results and answers. It handles potential errors during
-    agent execution and returns the results log and the answers payload.
-
-    Args:
-        gaia_agent (GaiaAgent): An instance of the GaiaAgent class, which is responsible for
-            generating answers to the questions.
-        questions_data (List[Dict]): A list of dictionaries, where each dictionary
-            represents a question and contains at least the 'task_id' and 'question' keys.
-
-    Returns:
-        Tuple[List[Dict], List[Dict]]: A tuple containing:
-            - A list of dictionaries representing the results log, where each dictionary
-              contains the 'Task ID', 'Question', and 'Submitted Answer'.
-            - A list of dictionaries representing the answers payload, where each dictionary
-              contains the 'task_id' and 'submitted_answer'.
-    """
-    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")
-        question_text = process_file(task_id, question_text)
-        if not task_id or question_text is None:
-            print(f"⚠️ Skipping invalid item (missing task_id or question): {item}")
-            continue
-        try:
-            submitted_answer = gaia_agent(task_id, question_text)
-            answers_payload.append(
-                {"task_id": task_id, "submitted_answer": submitted_answer}
-            )
-        except Exception as e:
-            print(f"❌ Error running agent on task {task_id}: {e}")
-            submitted_answer = f"AGENT ERROR: {e}"
-
-        results_log.append(
-            {
-                "Task ID": task_id,
-                "Question": question_text,
-                "Submitted Answer": submitted_answer,
-            }
-        )
-    return results_log, answers_payload
-
-
-def process_file(task_id: str, question_text: str) -> str:
-    """
-    Attempt to download a file associated with a task from the API.
-
-    - If the file exists (HTTP 200), it is saved to a temp directory and the local file path is returned.
-    - If no file is found (HTTP 404), returns None.
-    - For all other HTTP errors, the exception is propagated to the caller.
-    """
-    file_url = f"{FILE_PATH}{task_id}"
-
-    try:
-        response = requests.get(file_url, timeout=30)
-        response.raise_for_status()
-    except requests.exceptions.RequestException as exc:
-        print(f"Exception in download_file>> {str(exc)}")
-        return question_text # Unable to get the file
-
-    # Determine filename from 'Content-Disposition' header, fallback to task_id
-    content_disposition = response.headers.get("content-disposition", "")
-    filename = task_id
-    match = re.search(r'filename="([^"]+)"', content_disposition)
-    if match:
-        filename = match.group(1)
-
-    # Save file in a temp directory
-    temp_storage_dir = Path(tempfile.gettempdir()) / "gaia_cached_files"
-    temp_storage_dir.mkdir(parents=True, exist_ok=True)
-
-    file_path = temp_storage_dir / filename
-    file_path.write_bytes(response.content)
-    return (
-                f"{question_text}\n\n"
-                f"---\n"
-                f"A file was downloaded for this task and saved locally at:\n"
-                f"{str(file_path)}\n"
-                f"---\n\n"
+from typing import Dict, List, Tuple
+import re
+import tempfile
+from pathlib import Path
+import pandas as pd
+import requests
+from agent import GaiaAgent
+from pandas import DataFrame
+
+# --- Constants ---
+DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
+QUESTIONS_URL = f"{DEFAULT_API_URL}/questions"
+SUBMIT_URL = f"{DEFAULT_API_URL}/submit"
+FILE_PATH = f"{DEFAULT_API_URL}/files/"
+
+
+# --- Helper Methods ---
+def fetch_all_questions() -> Dict:
+    """Fetches all questions from the specified API endpoint.
+
+    This function retrieves a list of questions from the API, handles potential errors
+    such as network issues, invalid responses, or empty question lists, and returns
+    the questions as a dictionary.
+
+    Returns:
+        Dict: A dictionary containing the questions data retrieved from the API.
+
+    Raises:
+        UserWarning: If there is an error fetching the questions, such as network issues,
+            invalid JSON response, or an empty question list.  The exception message
+            provides details about the specific error encountered.
+    """
+    print(f"Fetching questions from: {QUESTIONS_URL}")
+    response = requests.get(QUESTIONS_URL, timeout=15)
+    try:
+        response.raise_for_status()
+        questions_data = response.json()
+        if not questions_data:
+            print("Fetched questions list is empty.")
+            raise UserWarning("Fetched questions list is empty or invalid format.")
+        print(f"Fetched {len(questions_data)} questions.")
+        return questions_data
+    except requests.exceptions.RequestException as e:
+        print(f"Error fetching questions: {e}")
+        raise UserWarning(f"Error fetching questions: {e}")
+    except requests.exceptions.JSONDecodeError as e:
+        print(f"Error decoding JSON response from questions endpoint: {e}")
+        print(f"Response text: {response.text[:500]}")
+        raise UserWarning(f"Error decoding server response for questions: {e}")
+    except Exception as e:
+        print(f"An unexpected error occurred fetching questions: {e}")
+        raise UserWarning(f"An unexpected error occurred fetching questions: {e}")
+
+
+def submit_answers(submission_data: dict, results_log: list) -> Tuple[str, DataFrame]:
+    """Submits answers to the scoring API and returns the submission status and results.
+
+    This function sends the provided answers to the scoring API, handles potential errors
+    such as network issues, server errors, or invalid responses, and returns a status
+    message indicating the success or failure of the submission, along with a DataFrame
+    containing the results log.
+
+    Args:
+        submission_data (dict): A dictionary containing the answers to be submitted.
+            Expected to have a structure compatible with the scoring API.
+        results_log (list): A list of dictionaries containing the results log.
+            This log is converted to a Pandas DataFrame and returned.
+
+    Returns:
+        Tuple[str, DataFrame]: A tuple containing:
+            - A status message (str) indicating the submission status and any relevant
+              information or error messages.
+            - A Pandas DataFrame containing the results log.
+
+    """
+    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"User: {result_data.get('username')}\n"
+            f"Overall Score: {result_data.get('score', 'N/A')}% "
+            f"({result_data.get('correct_count', '?')}/"
+            f"{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)
+        results_df = pd.DataFrame(results_log)
+        return status_message, results_df
+
+
+def run_agent(
+    gaia_agent: GaiaAgent, questions_data: List[Dict]
+) -> Tuple[List[Dict], List[Dict]]:
+    """Runs the agent on a list of questions and returns the results and answers.
+
+    This function iterates through a list of questions, runs the provided agent on each
+    question, and collects the results and answers. It handles potential errors during
+    agent execution and returns the results log and the answers payload.
+
+    Args:
+        gaia_agent (GaiaAgent): An instance of the GaiaAgent class, which is responsible for
+            generating answers to the questions.
+        questions_data (List[Dict]): A list of dictionaries, where each dictionary
+            represents a question and contains at least the 'task_id' and 'question' keys.
+
+    Returns:
+        Tuple[List[Dict], List[Dict]]: A tuple containing:
+            - A list of dictionaries representing the results log, where each dictionary
+              contains the 'Task ID', 'Question', and 'Submitted Answer'.
+            - A list of dictionaries representing the answers payload, where each dictionary
+              contains the 'task_id' and 'submitted_answer'.
+    """
+    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")
+        question_text = process_file(task_id, question_text)
+        if not task_id or question_text is None:
+            print(f"⚠️ Skipping invalid item (missing task_id or question): {item}")
+            continue
+        try:
+            submitted_answer = gaia_agent(task_id, question_text)
+            answers_payload.append(
+                {"task_id": task_id, "submitted_answer": submitted_answer}
+            )
+        except Exception as e:
+            print(f"❌ Error running agent on task {task_id}: {e}")
+            submitted_answer = f"AGENT ERROR: {e}"
+
+        results_log.append(
+            {
+                "Task ID": task_id,
+                "Question": question_text,
+                "Submitted Answer": submitted_answer,
+            }
+        )
+        # ✅ Add safe delay
+        if idx < len(questions_data):
+            time.sleep(3)
+    return results_log, answers_payload
+
+
+def process_file(task_id: str, question_text: str) -> str:
+    """
+    Attempt to download a file associated with a task from the API.
+
+    - If the file exists (HTTP 200), it is saved to a temp directory and the local file path is returned.
+    - If no file is found (HTTP 404), returns None.
+    - For all other HTTP errors, the exception is propagated to the caller.
+    """
+    file_url = f"{FILE_PATH}{task_id}"
+
+    try:
+        response = requests.get(file_url, timeout=30)
+        response.raise_for_status()
+    except requests.exceptions.RequestException as exc:
+        print(f"Exception in download_file>> {str(exc)}")
+        return question_text # Unable to get the file
+
+    # Determine filename from 'Content-Disposition' header, fallback to task_id
+    content_disposition = response.headers.get("content-disposition", "")
+    filename = task_id
+    match = re.search(r'filename="([^"]+)"', content_disposition)
+    if match:
+        filename = match.group(1)
+
+    # Save file in a temp directory
+    temp_storage_dir = Path(tempfile.gettempdir()) / "gaia_cached_files"
+    temp_storage_dir.mkdir(parents=True, exist_ok=True)
+
+    file_path = temp_storage_dir / filename
+    file_path.write_bytes(response.content)
+    return (
+                f"{question_text}\n\n"
+                f"---\n"
+                f"A file was downloaded for this task and saved locally at:\n"
+                f"{str(file_path)}\n"
+                f"---\n\n"
             )