app.py

import os
from typing import Optional, List, Dict, Any
import gradio as gr
from langchain_core.messages import HumanMessage
import requests
import pandas as pd
from agents.base_agent import agent_executor
import mimetypes
import base64

# (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, api_url: str = DEFAULT_API_URL):
        self.api_url = api_url
        print("BasicAgent initialized.")

    def _get_mime_type(self, file_content: bytes, filename: str) -> str:
        """Determine MIME type from file content and filename"""
        # Try to guess from filename first
        mime_type, _ = mimetypes.guess_type(filename)
        if mime_type:
            return mime_type

        # Fallback: check file headers for common types
        if file_content.startswith(b'\xff\xd8\xff'):
            return 'image/jpeg'
        elif file_content.startswith(b'\x89PNG\r\n\x1a\n'):
            return 'image/png'
        elif file_content.startswith(b'GIF8'):
            return 'image/gif'
        elif file_content.startswith(b'%PDF'):
            return 'application/pdf'
        elif file_content.startswith(b'RIFF') and b'WEBP' in file_content[:12]:
            return 'image/webp'
        else:
            return 'application/octet-stream'

    def _download_file(self, task_id: str) -> Optional[tuple]:
        """Download task's associated file"""
        try:
            files_url = f"{self.api_url}/files/{task_id}"
            print(f"Attempting to download file from {files_url}")

            response = requests.get(files_url, timeout=30)
            if response.status_code == 404:
                print('File not found for task ID:', task_id)
                return None

            response.raise_for_status()

            # try to get filename from Content-Disposition header
            filename = "file"
            if 'content-disposition' in response.headers:
                content_disposition = response.headers['content-disposition']
                if 'filename=' in content_disposition:
                    filename = content_disposition.split('filename=')[1].strip('"')

            file_content = response.content
            mime_type = self._get_mime_type(file_content, filename)

            print(f"Downloaded file: {filename} ({len(file_content)} bytes, {mime_type})")
            return file_content, filename, mime_type

        except requests.exceptions.RequestException as e:
            print(f"Error downloading file for task {task_id}: {e}")
            return None
        except Exception as e:
            print(f"Unexpected error downloading file for task {task_id}: {e}")
            return None


    def _create_multimodal_content(self, question: str, task_id: str) -> List[Dict[str, Any]]:
        """Create content blocks for multimodal input."""
        content_blocks = [{"type": "text", "text": question}]

        # Try to download associated file
        file_data = self._download_file(task_id)
        if file_data:
            file_content, filename, mime_type = file_data

            # Convert file content to base64
            base64_content = base64.b64encode(file_content).decode('utf-8')

            # Create appropriate content block based on file type
            if mime_type.startswith('image/'):
                content_blocks.append({
                    "type": "image",
                    "source_type": "base64",
                    "data": base64_content,
                    "mime_type": mime_type
                })
                print(f"Added image content block: {filename}")

            elif mime_type == 'application/pdf':
                content_blocks.append({
                    "type": "file",
                    "source_type": "base64",
                    "data": base64_content,
                    "mime_type": mime_type
                })
                print(f"Added PDF content block: {filename}")

            elif mime_type.startswith('audio/'):
                content_blocks.append({
                    "type": "audio",
                    "source_type": "base64",
                    "data": base64_content,
                    "mime_type": mime_type
                })
                print(f"Added audio content block: {filename}")

            elif mime_type.startswith('video/'):
                content_blocks.append({
                    "type": "video",
                    "source_type": "base64",
                    "data": base64_content,
                    "mime_type": mime_type
                })
                print(f"Added video content block: {filename}")

            else:
                # For other file types, add as generic file
                content_blocks.append({
                    "type": "file",
                    "source_type": "base64",
                    "data": base64_content,
                    "mime_type": mime_type
                })
                print(f"Added generic file content block: {filename} ({mime_type})")

            # Add context about the file to the text prompt
            content_blocks[0]["text"] += f"\n\nNote: I have attached a file named '{filename}' of type '{mime_type}'. Please analyze this file in the context of the question above."

        return content_blocks

    def __call__(self, question: str, task_id: str = "") -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        if task_id:
            print(f"Processing task_id: {task_id}")

        try:
            # Create multimodal content if task_id is provided
            if task_id:
                content = self._create_multimodal_content(question, task_id)
                message = HumanMessage(content=content)
            else:
                # Fallback to text-only
                message = HumanMessage(content=question)

            # Invoke the agent
            response = agent_executor.invoke({"messages": [message]})
            answer = response['messages'][-1].content

            return answer

        except Exception as e:
            print(f"Error in agent execution: {e}")
            # Fallback to text-only if multimodal fails
            try:
                message = HumanMessage(content=question)
                response = agent_executor.invoke({"messages": [message]})
                answer = response['messages'][-1].content
                return answer
            except Exception as fallback_error:
                print(f"Fallback also failed: {fallback_error}")
                return f"Error processing question: {e}"

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.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 requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching 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 with multimodal support on {len(questions_data)} questions...")

    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            # Pass both question and task_id to enable multimodal processing
            submitted_answer = agent(question_text, 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.  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)