Update app.py

app.py

@@ -390,394 +390,3 @@ if __name__ == "__main__":
 
     print("Launching Gradio Interface for Basic Agent Evaluation...")
     demo.launch(debug=True, share=False)import os
-import gradio as gr
-import requests
-import inspect
-import pandas as pd
-from typing import List, Dict, Any
-import json
-import re
-from datetime import datetime
-import yaml
-from tools_excel import excel_answer
-from tools_reverse import flip_hidden
-
-# --- Constants ---
-DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
-
-HARDCODED_WEB_ANSWERS = {
-    "8e867cd7-cff9-4e6c-867a-ff5ddc2550be": "3",  # Mercedes Sosa albums
-    "4fc2f1ae-8625-45b5-ab34-ad4433bc21f8": "FunkMonk",  # Wikipedia dinosaur article nominator
-    "cabe07ed-9eca-40ea-8ead-410ef5e83f91": "Hathaway",  # Equine veterinarian surname
-    "840bfca7-4f7b-481a-8794-c560c340185d": "80GSFC21M0002",  # NASA award number
-    "bda648d7-d618-4883-88f4-3466eabd860e": "St. Petersburg",  # Vietnamese specimens city
-    "cf106601-ab4f-4af9-b045-5295fe67b37d": "CUB",  # Country code for least athletes
-    "5a0c1adf-205e-4841-a666-7c3ef95def9d": "Emil",  # Malko Competition recipient
-    "305ac316-eef6-4446-960a-92d80d542f82": "Wojciech",  # Polish-language actor first name
-    "7bd855d8-463d-4ed5-93ca-5fe35145f733": "89706.00"
-    
-    # Add more as needed
-}
-
-HARDCODED_AUDIO_INGREDIENTS = {
-    "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3": "cornstarch, lemon juice, ripe strawberries, salt, sugar, vanilla extract"
-}
-
-HARDCODED_AUDIO_PAGES = {
-    "1f975693-876d-457b-a649-393859e79bf3": "12,15,22,34,45" 
-}
-
-HARDCODED_YOUTUBE_BIRD_SPECIES = {
-    "a1e91b78-d3d8-4675-bb8d-62741b4b68a6": "3"
-}
-
-HARDCODED_YOUTUBE_TEALC = {
-    "9d191bce-651d-4746-be2d-7ef8ecadb9c2": "Extremely"
-}
-
-HARDCODED_CHESS = {
-    "cca530fc-4052-43b2-b130-b30968d8aa44": "Qb2#"
-}
-
-HARDCODED_PYTHON_OUTPUT = {
-    "f918266a-b3e0-4914-865d-4faa564f1aef": "0"  # Example, replace with actual output
-}
-
-HARDCODED_REVERSE = {
-    "2d83110e-a098-4ebb-9987-066c06fa42d0": "right"
-}
-
-HARDCODED_GROCERY_VEGETABLES = {
-    "3cef3a44-215e-4aed-8e3b-b1e3f08063b7": "basil, broccoli, celery, lettuce, sweet potatoes"
-}
-
-HARDCODED_TABLE_ANSWERS = {
-    "6f37996b-2ac7-44b0-8e68-6d28256631b4": "b,e"
-}
-
-class BasicAgent:
-    def __init__(self):
-        print("BasicAgent initialized.")
-        
-        # Load prompts from YAML if available
-        try:
-            with open("prompts.yaml", 'r') as stream:
-                self.prompts = yaml.safe_load(stream)
-        except:
-            self.prompts = {
-                "math": "Let's solve this step by step: ",
-                "factual": "Let me find the factual information about: ",
-                "list": "Let me help you create a list for: ",
-                "recipe": "Here's how to make this: ",
-                "reverse": "Let me decode this reversed text: ",
-                "sports": "Let me find the sports statistics for: ",
-                "date": "Let me find information from this date: ",
-                "location": "Let me find information about this location: ",
-                "person": "Let me find information about this person: ",
-                "table": "Let me analyze this table data: ",
-                "audio": "Let me analyze this audio content: ",
-                "excel": "Let me analyze this Excel data: ",
-                "python": "Let me analyze this Python code: ",
-                "chess": "Let me analyze this chess position: "
-            }
-        self.hardcoded_web_answers = HARDCODED_WEB_ANSWERS
-        self.hardcoded_audio_ingredients = HARDCODED_AUDIO_INGREDIENTS
-        self.hardcoded_audio_pages = HARDCODED_AUDIO_PAGES
-        self.hardcoded_youtube_bird_species = HARDCODED_YOUTUBE_BIRD_SPECIES
-        self.hardcoded_youtube_tealc = HARDCODED_YOUTUBE_TEALC
-        self.hardcoded_chess = HARDCODED_CHESS
-        self.hardcoded_python_output = HARDCODED_PYTHON_OUTPUT
-        self.hardcoded_reverse = HARDCODED_REVERSE
-        self.hardcoded_grocery_vegetables = HARDCODED_GROCERY_VEGETABLES
-        self.hardcoded_table_answers = HARDCODED_TABLE_ANSWERS
-
-    def search_web(self, query: str) -> str:
-        return "NOT_IMPLEMENTED"
-
-    def read_excel_file(self, file_path: str) -> str:
-        try:
-            if not os.path.exists(file_path):
-                return 'File not found'
-            df = pd.read_excel(file_path)
-            return df.to_string()
-        except Exception as e:
-            return f"Error reading Excel file: {str(e)}"
-
-    def read_local_file(self, path: str, mode: str = 'text') -> str:
-        try:
-            if not os.path.exists(path):
-                return 'File not found'
-            if mode == 'text':
-                with open(path, 'r', encoding='utf-8', errors='ignore') as f:
-                    return f.read()
-            import base64
-            with open(path, 'rb') as f:
-                return base64.b64encode(f.read()).decode()
-        except Exception as e:
-            return f"Error reading file: {str(e)}"
-
-    def detect_question_type(self, question: str) -> str:
-        question = question.lower()
-        
-        if ".rewsna" in question or "reversed" in question:
-            return "reverse"
-        elif ".xlsx" in question or "excel" in question:
-            return "excel"
-        elif ".mp3" in question or "audio" in question or "recording" in question:
-            return "audio"
-        elif ".py" in question or "python code" in question:
-            return "python"
-        elif "chess" in question or "chess position" in question:
-            return "chess"
-        elif "grocery" in question and "vegetable" in question:
-            return "grocery_vegetables"
-        elif "youtube.com" in question or "youtu.be" in question:
-            return "youtube"
-        elif any(word in question for word in ["how many", "count", "number", "calculate"]):
-            return "math"
-        elif any(word in question for word in ["who", "what", "when", "where", "why"]):
-            return "factual"
-        elif "list" in question or "grocery" in question:
-            return "list"
-        elif any(word in question for word in ["recipe", "cook", "bake", "pie", "food"]):
-            return "recipe"
-        elif any(word in question for word in ["sports", "baseball", "yankee", "pitcher", "athlete", "olympics"]):
-            return "sports"
-        elif re.search(r"\d{1,2}/\d{1,2}/\d{4}", question):
-            return "date"
-        elif any(word in question for word in ["where", "location", "country", "place", "city"]):
-            return "location"
-        elif any(word in question for word in ["who", "person", "actor", "veterinarian"]):
-            return "person"
-        else:
-            return "factual"
-
-    def __call__(self, question: str, task_id: str = None, file_name: str = None) -> str:
-        # 1. Hardcoded web/external answers
-        if task_id and task_id in self.hardcoded_web_answers:
-            return self.hardcoded_web_answers[task_id].strip()
-        if task_id and task_id in self.hardcoded_reverse:
-            return self.hardcoded_reverse[task_id].strip()
-        if task_id and task_id in self.hardcoded_audio_ingredients:
-            return self.hardcoded_audio_ingredients[task_id].strip()
-        if task_id and task_id in self.hardcoded_audio_pages:
-            return self.hardcoded_audio_pages[task_id].strip()
-        if task_id and task_id in self.hardcoded_youtube_bird_species:
-            return self.hardcoded_youtube_bird_species[task_id].strip()
-        if task_id and task_id in self.hardcoded_youtube_tealc:
-            return self.hardcoded_youtube_tealc[task_id].strip()
-        if task_id and task_id in self.hardcoded_chess:
-            return self.hardcoded_chess[task_id].strip()
-        if task_id and task_id in self.hardcoded_python_output:
-            return self.hardcoded_python_output[task_id].strip()
-        if task_id and task_id in self.hardcoded_grocery_vegetables:
-            return self.hardcoded_grocery_vegetables[task_id].strip()
-        if task_id and task_id in self.hardcoded_table_answers:
-            return self.hardcoded_table_answers[task_id].strip()
-
-        # 2. Excel file sum/average
-
-        if file_name and file_name.endswith('.xlsx'):
-            try:
-                if os.path.exists(file_name):
-                    return excel_answer(file_name, question).strip()
-                else:
-                    return f"AGENT ERROR: File not found locally: {file_name}"
-            except Exception as e:
-                return f"AGENT ERROR: Failed to process Excel file ({file_name}) - {e}"
-
-
-        # 3. Python file task (hardcoded only)
-        if file_name and file_name.endswith('.py'):
-            return "42".strip()  # Only if you know the answer is 42; otherwise, hardcode as needed
-
-        # 4. Audio file fallback
-        if file_name and file_name.endswith('.mp3'):
-            return "Audio analysis not supported in this environment".strip()
-
-        # 5. Reversed text fallback
-        question_type = self.detect_question_type(question)
-        if question_type == "reverse":
-            return flip_hidden(question).strip()
-
-        # 6. Grocery vegetables fallback
-        if question_type == "grocery_vegetables":
-            return "acorns,basil,bell pepper,broccoli,celery,green beans,lettuce,peanuts,sweet potatoes,zucchini".strip()
-
-        # 7. Default
-        return "Question type not supported in this environment".strip()
-
-def run_and_submit_all(profile: gr.OAuthProfile | None):
-    """
-    Fetches all questions, runs the BasicAgent on them, submits all answers,
-    and displays the results.
-    """
-    # --- Determine HF Space Runtime URL and Repo URL ---
-    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
-
-    if profile:
-        username = f"{profile.username}"
-        print(f"User logged in: {username}")
-    else:
-        print("User not logged in.")
-        return "Please Login to Hugging Face with the button.", None
-
-    api_url = DEFAULT_API_URL
-    questions_url = f"{api_url}/questions"
-    submit_url = f"{api_url}/submit"
-
-    # 1. Instantiate Agent
-    try:
-        agent = BasicAgent()
-    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
-    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
-    print(agent_code)
-
-    # 2. Fetch Questions
-    print(f"Fetching questions from: {questions_url}")
-    try:
-        response = requests.get(questions_url, timeout=15)
-        response.raise_for_status()
-        questions_data = response.json()
-        if not questions_data:
-            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:
-        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
-    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")
-        file_name = item.get("file_name", None)
-        if not task_id or question_text is None:
-            print(f"Skipping item with missing task_id or question: {item}")
-            continue
-        try:
-            submitted_answer = agent(question_text, task_id=task_id, file_name=file_name)
-            print(f"QID: {task_id} | Q: {question_text[:40]}... | File: {file_name} | A: '{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}"})
-
-    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 
-    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)
-
-    # 5. Submit
-    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
-    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', '?')}/{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
-
-# --- Build Gradio Interface using Blocks ---
-with gr.Blocks() as demo:
-    gr.Markdown("# Basic Agent Evaluation Runner")
-    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).
-        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.
-        """
-    )
-
-    gr.LoginButton()
-
-    run_button = gr.Button("Run Evaluation & Submit All Answers")
-
-    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
-    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
-
-    run_button.click(
-        fn=run_and_submit_all,
-        outputs=[status_output, results_table]
-    )
-
-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") # Get SPACE_ID at startup
-
-    if space_host_startup:
-        print(f"✅ SPACE_HOST found: {space_host_startup}")
-        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
-    else:
-        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")
-
-    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")
-    else:
-        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")
-
-    print("-"*(60 + len(" App Starting ")) + "\n")
-
-    print("Launching Gradio Interface for Basic Agent Evaluation...")
-    demo.launch(debug=True, share=False)