app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
import asyncio
from langchain_google_genai.chat_models import ChatGoogleGenerativeAI
from typing import IO, Dict
from io import BytesIO
from langchain_core.messages import HumanMessage, SystemMessage
from langgraph.graph import MessagesState
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import tools_condition
from langgraph.prebuilt import ToolNode
import base64
from google.ai.generativelanguage_v1beta.types import Tool as GenAITool
from google import genai
from google.genai import types
import os

GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")

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

# --- Basic Agent Definition ---
# Agent capabilities required: Search the web, listen to audio recordings, watch YouTube videos (process the footage, not the transcript), work with Excel spreadsheets
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------

def get_file(task_id: str) -> IO:
    '''
    Downloads the file associated with the given task_id, if one exists and is mapped.
    If the question mentions an attachment, use this function.
    Args:
        task_id: Id of the question.
    Returns:
        The file associated with the question.
    '''
    file_request = requests.get(url=f'https://agents-course-unit4-scoring.hf.space/files/{task_id}')
    file_request.raise_for_status()

    return BytesIO(file_request.content)

def analyse_excel(task_id: str) -> Dict[str, float]:
    '''
    Analyzes the Excel file associated with the given task_id and returns the sum of each numeric column.
    Args:
        task_id: Id of the question.
    Returns:
        A dictionary with the sum of each numeric column.
    '''
    excel_file = get_file(task_id)
    df = pd.read_excel(excel_file, sheet_name=0)
    
    return df.select_dtypes(include='number').sum().to_dict()

def add_numbers(a: float, b: float) -> float:
    '''
    Adds two numbers together.
    Args:
        a: First number.
        b: Second number.
    Returns:
        The sum of the two numbers.
    '''
    return a + b

def transcribe_audio(task_id: str) -> HumanMessage:
    '''
    Opens an audio file and returns its content as a string.
    Args:
        file: The audio file to be opened.
    Returns:
        The content of the audio file as a string.
    '''
    audio_file = get_file(task_id)
    if audio_file is None:
        raise ValueError("No audio file found for the given task_id.")
    # Encode the audio file to base64
    audio_file.seek(0)  # Ensure the file pointer is at the beginning
    encoded_audio = base64.b64encode(audio_file.read()).decode("utf-8")

    return HumanMessage(
        content=[
            {"type": "text", "text": "Transcribe the audio."},
            {
                "type": "media",
                "data": encoded_audio,  # Use base64 string directly
                "mime_type": "audio/mpeg",
            },
        ]
    )

def python_code(task_id: str) -> str:
    '''
    Returns the Python code associated with the given task_id.
    Args:
        task_id: Id of the question.
    Returns:
        The Python code associated with the question.
    '''
    code_request = requests.get(url=f'https://agents-course-unit4-scoring.hf.space/files/{task_id}')
    code_request.raise_for_status()
    
    return code_request.text

def open_image(task_id: str) -> str:
    '''
    Opens an image file associated with the given task_id.
    Args:
        task_id: Id of the question.
    Returns:
        The base64 encoded string of the image file.
    '''
    image_file = get_file(task_id)
    if image_file is None:
        raise ValueError("No image file found for the given task_id.")
    
    return base64.b64encode(image_file.read()).decode("utf-8")

def open_youtube_video(url: str, query:str) -> str:
    '''
    Answers a question about a video from the given URL.
    Args:
        url: The URL of the video file.
        query: The question to be answered about the video.
    Returns:
        Answer to the question about the video.
    '''
    
    client = genai.Client(api_key=GOOGLE_API_KEY)

    response = client.models.generate_content(
    model='models/gemini-2.0-flash',
    contents=types.Content(
        parts=[
            types.Part(
                file_data=types.FileData(file_uri=url)
            ),
            types.Part(text=f'''{query} YOUR FINAL ANSWER should be a number OR as few words as possible OR a comma separated 
                       list of numbers and/or strings.''')
            ]
            )
            )
    
    return response.text

def google_search(query: str) -> str:
    '''
    Performs a Google search for the given query.
    Args:
        query: The search query.
    Returns:
        The search results as a string.
    '''
    llm = ChatGoogleGenerativeAI(
            model="gemini-2.5-flash-preview-05-20",
            max_tokens=8192,
            temperature=0
            )
    response = llm.invoke(query,
        tools=[GenAITool(google_search={})]
    )

    return response.content


class BasicAgent:
    def __init__(self):
        self.llm = ChatGoogleGenerativeAI(
            model="gemini-2.5-flash-preview-05-20",
            max_tokens=8192,
            temperature=0
            )
        self.tools = [get_file, analyse_excel, add_numbers, transcribe_audio, python_code, open_image, open_youtube_video
         , google_search
         ]
        
        self.agent = self.llm.bind_tools(self.tools)

        self.sys_msg = SystemMessage('''You are a general AI assistant. I will ask you a question. Only provide YOUR FINAL ANSWER and nothing else. 
        YOUR FINAL ANSWER should be 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.
        You have access to multiple tools and should use as many as you need to answer the question.
        If you are asked to analyze an Excel file, use the 'analyse_excel' tool. 
        If you are asked to download a file, use the 'get_file' tool. 
        If you are asked to add two numbers, use the 'add_numbers' tool. If you need to add more than two numbers, use the 'add_numbers' 
        tool multiple times.
        If you are asked to transcribe an audio file, use the 'transcribe_audio' tool.
        If you are asked to run a Python code, use the 'python_code' tool.
        If you are asked to open an image, use the 'open_image' tool.
        If you were given a link with www.youtube.com, use the 'open_youtube_video' tool.
        If the question requires a web search because your internal knowledge doesn't have the information, use the 'google_search' tool.
                        ''')

        # Graph
        self.builder = StateGraph(MessagesState)

        # Define nodes: these do the work
        self.builder.add_node("assistant", self.assistant)
        self.builder.add_node("tools", ToolNode(self.tools))

        # Define edges: these determine how the control flow moves
        self.builder.add_edge(START, "assistant")
        self.builder.add_conditional_edges(
            "assistant",
            # If the latest message (result) from assistant is a tool call -> tools_condition routes to tools
            # If the latest message (result) from assistant is a not a tool call -> tools_condition routes to END
            tools_condition,
        )
        self.builder.add_edge("tools", "assistant")
        self.react_graph = self.builder.compile()
        
        print("BasicAgent initialized.")
    
    def assistant(self, state: MessagesState):
        return {"messages": [self.agent.invoke([self.sys_msg] + state["messages"])]}
    
    async def __call__(self, question: str, task_id: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = "This is a default answer."

        await asyncio.sleep(60)
        messages = self.react_graph.invoke({"messages": f'Task id: {task_id}\n {question}'})
        return messages["messages"][-1].content if messages["messages"] else 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")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue
        try:
            submitted_answer = asyncio.run(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)