app.py

import base64
import inspect
import mimetypes
import os
import tempfile

import gradio as gr
import pandas as pd
import requests
from langchain_community.document_loaders import UnstructuredExcelLoader

from agents import supervisor_agent

# (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 ------
def fetch_file(task_id):
    if not task_id:
        return None
    url = f"https://agents-course-unit4-scoring.hf.space/files/{task_id}"
    response = requests.get(url)
    if response.status_code == 200:
        return response.content  # Return raw bytes
    else:
        print(f"Failed to fetch file for task_id {task_id}: {response.status_code}")
        return None

def build_multimodal_message(question, file_bytes=None, file_name=None):
    """
    Build a multimodal message with correct content blocks for text, image, audio, or file.
    For .xlsx files, extract the text and append it to the question, since LLMs do not natively support .xlsx.
    Follows: https://python.langchain.com/docs/how_to/multimodal_inputs/
    """
    content = []
    # Special handling for .xlsx files
    if file_bytes and file_name and file_name.lower().endswith('.xlsx'):
        with tempfile.NamedTemporaryFile(delete=False, suffix='.xlsx') as tmp:
            tmp.write(file_bytes)
            tmp_path = tmp.name
        loader = UnstructuredExcelLoader(tmp_path, mode="elements")
        docs = loader.load()
        excel_text = "\n".join(doc.page_content for doc in docs)
        question = f"{question}\n\n[Excel file content follows:]\n{excel_text}"
    content.append({"type": "text", "text": question})
    if file_bytes and file_name and not file_name.lower().endswith('.xlsx'):
        ext = file_name.lower().split('.')[-1]
        b64_data = base64.b64encode(file_bytes).decode("utf-8")
        mime_type, _ = mimetypes.guess_type(file_name)
        # Handle common audio/image types explicitly
        if ext in ["png"]:
            mime_type = "image/png"
            block_type = "image"
        elif ext in ["jpg", "jpeg"]:
            mime_type = "image/jpeg"
            block_type = "image"
        elif ext == "mp3":
            mime_type = "audio/mpeg"
            block_type = "audio"
        elif ext == "wav":
            mime_type = "audio/wav"
            block_type = "audio"
        elif ext == "m4a":
            mime_type = "audio/mp4"
            block_type = "audio"
        else:
            block_type = "file"
            if not mime_type:
                mime_type = "application/octet-stream"
        block = {
            "type": block_type,
            "source_type": "base64",
            "data": b64_data,
            "mime_type": mime_type,
            "filename": file_name,
        }
        content.append(block)
    return [{"role": "user", "content": content}]

def filter_supported_content_blocks(messages):
    allowed_types = {"text", "image_url", "input_audio", "refusal", "audio", "file", "image"}
    filtered = []
    for msg in messages:
        if "content" in msg and isinstance(msg["content"], list):
            filtered_content = [block for block in msg["content"] if block.get("type") in allowed_types]
            msg = dict(msg)
            msg["content"] = filtered_content
        filtered.append(msg)
    return filtered

class Agent:
    def __init__(self):
        self.main_agent = supervisor_agent
        print("Agent initialized.")
    def __call__(self, question: str, file_name: str = "", task_id: str = "") -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        file_bytes = fetch_file(task_id) if file_name else None
        message = build_multimodal_message(question, file_bytes, file_name)
        # Filter out unsupported content block types
        message = filter_supported_content_blocks(message)
        result = self.main_agent.invoke({"messages": message})
        answer = result["messages"][-1]
        content = answer.content
        if isinstance(content, list) and content and isinstance(content[0], dict) and "text" in content[0]:
            return content[0]["text"]
        elif isinstance(content, str):
            return content
        else:
            return str(content)

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 = Agent()
    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", "")
        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, task_id)
            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.  Enter your OpenAI and Google API keys below (these are required for the agent to work).
        4.  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.
        """
    )

    gr.LoginButton()

    openai_key_box = gr.Textbox(label="OpenAI API Key", type="password", placeholder="sk-...", lines=1)
    google_key_box = gr.Textbox(label="Google API Key", type="password", placeholder="AIza...", lines=1)
    set_keys_btn = gr.Button("Set API Keys")
    status_api_keys = gr.Textbox(label="API Key Status", lines=1, interactive=False)

    def set_api_keys(openai_key, google_key):
        if openai_key:
            os.environ["OPENAI_API_KEY"] = openai_key
        if google_key:
            os.environ["GOOGLE_API_KEY"] = google_key
        if openai_key or google_key:
            return "API keys set for this session."
        return "No API keys provided."

    set_keys_btn.click(
        fn=set_api_keys,
        inputs=[openai_key_box, google_key_box],
        outputs=status_api_keys
    )

    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)