app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from typing import TypedDict, Annotated, Union, Dict, Any
from smolagents import DuckDuckGoSearchTool
from langchain_community.tools import DuckDuckGoSearchRun
from tools import get_hub_stats, analyze_image, read_excel_file, download_file, execute_python_code, transcribe_audio
from langchain.tools import Tool
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
from langchain_openai import ChatOpenAI
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage, AIMessage
from langgraph.graph import START, StateGraph
from langgraph.graph.message import add_messages
from langgraph.prebuilt import ToolNode, tools_condition

# (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.")
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = "This is a default answer."
        print(f"Agent returning fixed answer: {fixed_answer}")
        return fixed_answer

class NewAgent:
    def __init__(self):
        print("NewAgent initialized.")
    def __call__(self, question: str, file_name: str = "", task_id: str = "") -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        # Auto-download file if file_name and task_id are provided
        local_file_path = ""
        if file_name and task_id:
            print(f"Auto-downloading file: {file_name} for task: {task_id}")
            local_file_path = download_file(task_id, file_name)
            if local_file_path.startswith("Error"):
                print(f"File download failed: {local_file_path}")
            else:
                print(f"File downloaded successfully to: {local_file_path}")
        
        # Initialize the web search tool
        search_tool = DuckDuckGoSearchRun()
        # Initialize the Hub stats tool
        hub_stats_tool = Tool(
            name="get_hub_stats",
            func=get_hub_stats,
            description="Fetches the most downloaded model from a specific author on the Hugging Face Hub."
        )
        # Initialize the Image Analysis tool
        image_analysis_tool = Tool(
            name="analyze_image",
            func=analyze_image,
            description="Analyzes images and answers questions about their content. Input should be the path to an image file."
            )
        # Initialize the Read Excel tool
        read_excel_tool = Tool(
            name="read_excel_file",
            func=read_excel_file,
            description="Reads an Excel file and returns structured information about its contents."
        )
        # Initialize the Python Execution tool
        python_exec_tool = Tool(
            name="execute_python_code",
            func=execute_python_code,
            description="Executes a Python file and returns its output. Input should be the path to a Python file."
        )
        # Initialize the Python Execution tool
        transcribe_audio_tool = Tool(
            name="transcribe_audio",
            func=transcribe_audio,
            description="Transcribe audio file using OpenAI Whisper"
        )
        # Generate the chat interface, including the tools
        tools = [
            search_tool,
            hub_stats_tool,
            image_analysis_tool,
            read_excel_tool,
            python_exec_tool,
            transcribe_audio_tool,
        ]
        # llm = ChatOpenAI(model="gpt-4o")
        llm = ChatOpenAI(model="gpt-4.1")
        llm_with_tools = llm.bind_tools(tools, parallel_tool_calls=False)
        
        # Generate the AgentState and Agent graph
        class AgentState(TypedDict):
            messages: Annotated[list[AnyMessage], add_messages]
        def assistant(state: AgentState):
            sys_msg = SystemMessage(
                content=f"""
                You are a general AI assistant. I will ask you a question. 
                If a file_name is provided, it indicates there's an associated file you may need to analyze.
                You can download files using the download_file tool with the format 'task_id,filename'.
                After downloading, you can analyze images with analyze_image or Excel files with read_excel_file.
                If you cannot find an answer, you may report your thoughts and an explanation why. 
                If there is an error, such as a missing dependancy, return the error message.
                If you find an answer, your response should only contain your final answer. Report nothing before or after this 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.
                Current task_id: {task_id}
                Current file (if any): {file_name}
                Downloaded file path: {local_file_path}
                """
                                   )
            return {
                "messages": [llm_with_tools.invoke([sys_msg] + state["messages"])],
            }
            
        ## The graph
        builder = StateGraph(AgentState)
        # 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",
            # If the latest message requires a tool, route to tools
            # Otherwise, provide a direct response
            tools_condition,
        )
        builder.add_edge("tools", "assistant")
        alfred = builder.compile()
        
        messages = [HumanMessage(content=question)]
        response = alfred.invoke({"messages": messages})
        return response['messages'][-1].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 = BasicAgent()
        agent = NewAgent()
        # agent = NewAgent2()
    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, "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.')}"
            f"\n\n"
            f"result_data: {result_data}"
        )
        print(result_data)
        print(f"\n\n")
        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)