app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from langchain_openai import ChatOpenAI
from langchain_core.messages import AnyMessage, HumanMessage, AIMessage, SystemMessage
from langgraph.graph import START, StateGraph
from langgraph.prebuilt import ToolNode, tools_condition
from langgraph.graph.message import add_messages
from typing import TypedDict, Annotated
from tools import (
    serp_search_tool,
    python_execution_tool,
    reverse_text_tool,
    audio_processing_tool,
    video_analysis_tool,
    image_recognition_tool,
    file_type_detection_tool,
    read_file_tool,
    code_execution_tool,
    math_calculation_tool,
    wiki_search_tool,
    python_repl_tool,
    extract_text_from_image_tool,
    analyze_csv_file_tool,
    analyze_excel_file_tool,
)
import re
import tempfile

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

# Setting up the llm
llm = ChatOpenAI(model="gpt-4o", temperature=0)
tools = [
    serp_search_tool,
    wiki_search_tool,
    python_execution_tool,
    reverse_text_tool,
    audio_processing_tool,
    video_analysis_tool,
    image_recognition_tool,
    file_type_detection_tool,
    read_file_tool,
    code_execution_tool,
    math_calculation_tool,
    python_repl_tool,
    extract_text_from_image_tool,
    analyze_csv_file_tool,
    analyze_excel_file_tool,
]
chat_with_tools = llm.bind_tools(tools)

# Defining my agent


class MyAgent(TypedDict):
    messages: Annotated[list[AnyMessage], add_messages]


# =========================
# Efficient File Handling - Download with Question
# =========================
def process_question_with_files(question_data: dict) -> str:
    """
    Download file content when processing the question and include it directly.
    This eliminates the need for the agent to download files separately.
    """
    question_text = question_data.get('question', '')
    file_name = question_data.get('file_name', '')
    task_id = question_data.get('task_id', '')

    if not file_name:
        return question_text

    print(f"📎 Downloading file for question: {file_name}")

    try:
        # Download the file content directly
        file_url = f"{DEFAULT_API_URL}/files/{task_id}"
        response = requests.get(file_url, timeout=15)
        response.raise_for_status()

        # Save file to temporary location for processing
        temp_dir = tempfile.gettempdir()
        local_file_path = os.path.join(temp_dir, file_name)

        with open(local_file_path, "wb") as f:
            f.write(response.content)

        # Process the file based on its type
        ext = file_name.lower().split('.')[-1]

        if ext in ['mp3', 'wav', 'm4a', 'flac', 'ogg']:
            result = audio_processing_tool.invoke(local_file_path)
            file_info = f"[Audio Transcription: {result}]"
        elif ext in ['png', 'jpg', 'jpeg', 'gif', 'bmp']:
            result = image_recognition_tool.invoke(local_file_path)
            file_info = f"[Image Analysis: {result}]"
        elif ext in ['csv', 'xls', 'xlsx']:
            result = read_file_tool.invoke(local_file_path)
            file_info = f"[Spreadsheet Content: {result}]"
        elif ext in ['txt', 'md', 'py', 'json']:
            result = read_file_tool.invoke(local_file_path)
            file_info = f"[File Content: {result}]"
        else:
            result = read_file_tool.invoke(local_file_path)
            file_info = f"[File Content: {result}]"

        # Clean up the temporary file
        try:
            os.remove(local_file_path)
        except Exception:
            pass

        return f"{question_text}\n\n{file_info}"

    except Exception as e:
        print(f"Error downloading/processing file {file_name}: {e}")
        return f"{question_text}\n\n[Note: Could not download or process attached file {file_name}: {str(e)}]"


def extract_final_answer(text: str) -> str:
    # Remove common prefixes
    text = re.sub(r'(?i)(answer:|final answer:|the answer is:)', '', text)
    # Remove repeated question lines
    lines = [line for line in text.strip().split(
        '\n') if not line.strip().endswith('?')]
    # If the answer is a number at the end, return it
    match = re.search(r'\b\d+\b$', text.strip())
    if match:
        return match.group(0)
    # If the answer is a comma-separated list, return it
    if ',' in text and len(text.split(',')) <= 10:
        return ','.join([x.strip() for x in text.split(',') if x.strip()])
    # Otherwise, return the last non-empty line
    for line in reversed(lines):
        if line.strip():
            return line.strip()
    return text.strip()


class AgentWrapper:
    def __init__(self):
        print("AgentWrapper initialized.")

    def __call__(self, question_data: dict | str) -> str:
        if isinstance(question_data, str):
            question_text = question_data
            print(
                f"Agent received question (first 50 chars): {question_text[:50]}...")
        else:
            question_text = process_question_with_files(question_data)
            print(
                f"Agent received enhanced question (first 50 chars): {question_text[:50]}...")
        try:
            result = my_agent.invoke(
                {"messages": [HumanMessage(content=question_text)]})
            last_message = result["messages"][-1]
            answer = last_message.content
            final_answer = extract_final_answer(answer)
            print(f"Agent returning answer: {final_answer}")
            return final_answer
        except Exception as e:
            print(f"Error in agent processing: {e}")
            return f"Error processing question: {e}"

# set the main system prompt


def assistant(state: MyAgent):
    # Add system message to instruct the agent to use the tool
    system_message = SystemMessage(content="""
You are a general AI assistant. I will ask you a question. Report your thoughts, and finish
your answer with the following template: FINAL ANSWER: [YOUR FINAL ANSWER].
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.
""")

    # Combine system message with user messages
    all_messages = [system_message] + state["messages"]

    return {
        "messages": [chat_with_tools.invoke(all_messages)],
    }


# define the agent graph
builder = StateGraph(MyAgent)

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

# Define edges: these determine how the control flow moves
builder.add_edge(START, "assistant")
builder.add_conditional_edges(
    "assistant",
    tools_condition,
)
builder.add_edge("tools", "assistant")
my_agent = builder.compile()

# submit


def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs MyAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    # Get the SPACE_ID for sending link to the code
    space_id = os.getenv("SPACE_ID")

    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 = AgentWrapper()
    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

        # Create complete question data for the agent
        question_data = {
            "task_id": task_id,
            "question": question_text,
            "file_name": file_name
        }

        try:
            submitted_answer = agent(question_data)
            answers_payload.append(
                {"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append(
                {"Task ID": task_id, "Question": question_text, "File": file_name, "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, "File": file_name, "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("# MyAgent 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)


# test
messages = [HumanMessage(
    content="Question: How many studio albums were published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest 2022 version of english wikipedia.")]
response = my_agent.invoke({"messages": messages})
print("🎩 Alfred's Response:")
print(response['messages'][-1].content)