app.py

import os
import gradio as gr
import requests
from io import BytesIO
from PIL import Image
import inspect
import yaml
import pandas as pd

from smolagents import (
    OpenAIServerModel, 
    ToolCallingAgent, 
    CodeAgent, 
    HfApiModel, 
    DuckDuckGoSearchTool, 
    WebSearchTool, 
    VisitWebpageTool, 
    SpeechToTextTool,
    AgentAudio,
    PythonInterpreterTool,
)
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()

# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
class BasicAgent:
    def __init__(self):
        print("BasicAgent initialized.")
        
        # Initialize the agent with a model and tools
        # model = OpenAIServerModel(
        #     model_id=os.environ["MODEL_ID"],
        #     api_base="https://api.openai.com/v1",
        #     api_key=os.environ["OPENAI_API_KEY"],
        # )
        model = OpenAIServerModel(
            model_id=os.environ["MODEL_ID"],
            api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
            api_key=os.environ["GEMINI_API_KEY"],
        )

        web_agent = ToolCallingAgent(
            verbosity_level=1,
            tools=[WebSearchTool(), VisitWebpageTool()],
            max_steps=5,
            model=model,
            name="web_search_agent",
            description="This agent can search the web and visit webpages to gather information.",
        )

        stt_agent = ToolCallingAgent(
            verbosity_level=1,
            tools=[SpeechToTextTool()],
            max_steps=5,
            model=model,
            name="speech_to_text_agent",
            description="This agent can transcribe audio files to text.",
        )

        manager_agent = CodeAgent(
            tools=[],
            model=model,
            managed_agents=[web_agent, stt_agent],
            additional_authorized_imports=["time", "numpy", "pandas"],
        )

        self.agent = manager_agent
        print(f"Agent initialized with model ID: {os.environ['MODEL_ID']}")
        print(f"Agent initialized with tools: {self.agent.tools}")

    def __call__(self, question: str, file_name: str, file_type: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        system_prompt = "You are a general AI assistant. I will ask you a question. Report your thoughts, and finish your answer as a number OR as few words as possible OR a comma separated list of numbers and/or strings. If you are asked for a number, don't use comma to write your number neither use units such as $ or percent sign unless specified otherwise. If you are asked for a string, don't use articles, neither abbreviations (e.g. for cities), and write the digits in plain text unless specified otherwise. If you are asked for a comma separated list, apply the above rules depending of whether the element to be put in the list is a number or a string. Here is the question: "

        if file_type == "image":
            # If the file is an image, read file_name and convert it to a PIL Image
            image = Image.open(file_name)
            image = image.convert("RGB")
            # Convert the image to bytes
            image_bytes = BytesIO()
            answer = self.agent.run(system_prompt + question, images=[image_bytes])
        elif file_type == "audio":
            arguments = {"audio": file_name}
            answer = self.agent.run(system_prompt + question, additional_args=arguments)
        elif file_type == "python":
            with open(file_name, "r") as file:
                python_code = file.read()
            answer = self.agent.run(system_prompt + question, additional_args={"code": python_code})
        else:
            answer = self.agent.run(system_prompt + question)

        if answer:
            print(f"Agent returning answer: {answer}")
            return answer
        else:
            print("Agent returned no answer, returning fixed answer.")
            # Fallback to a fixed answer if the agent does not return anything
            fixed_answer = "This is a default answer."
            print(f"Agent returning fixed answer: {fixed_answer}")
            return fixed_answer

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 ( modify this part to create your 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 ( usefull for others so please keep it public)
    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")
        file_type = "unknown"

        if file_name:
            print(f"Fetching file content '{file_name}' for task ID: {task_id}")
            try:
                file_url = f"{api_url}/files/{task_id}"
                file_response = requests.get(file_url, timeout=15)
                file_response.raise_for_status()

                # parse the file extension for the file name to see if it is an image, audio, or python file
                file_extension = os.path.splitext(file_name)[1].lower()
                if file_extension in ['.jpg', '.jpeg', '.png', '.gif']:
                    # If the file is an image, convert it to a PIL Image
                    file_type = "image"

                    question_text = f"Here is an image: {file_name}. Please describe it."
                    # Save the image to a local file
                    with open(file_name, "wb") as image_file:
                        image_file.write(file_response.content)
                        print(f"Saved image file: {file_name}")
                elif file_extension in ['.wav', '.mp3', '.ogg']:
                    # If the file is an audio file, convert it to text
                    file_type = "audio"
                    audio_data = file_response.content
                    question_text = f"Here is an audio file: {file_name}. Please transcribe it."

                    # Save the audio to a local file 
                    with open(file_name, "wb") as audio_file:
                        audio_file.write(file_response.content)
                        print(f"Saved audio file: {file_name}")
                elif file_extension in ['.py']:
                    # If the file is a Python file, you might want to run it or analyze it
                    file_type = "python"
                    question_text = f"Here is a Python file: {file_name}. Please analyze it."

                    # Save the Python file to a local file
                    with open(file_name, "wb") as python_file:
                        python_file.write(file_response.content)
                        print(f"Saved Python file: {file_name}")

            except requests.exceptions.HTTPError as e:
                print(f"Error fetching file for task ID {task_id}: {e}")
                continue
            except requests.exceptions.RequestException as e:
                print(f"Error fetching file for task ID {task_id}: {e}")
                continue


        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, file_name, file_type)
            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)
    # 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,
        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)