app.py

import os
import gradio as gr
import requests
import inspect
import pandas as pd
from langgraph.graph import StateGraph, START, END
from typing_extensions import TypedDict
from typing import List, TypedDict, Annotated, Optional
from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
from langgraph.graph.message import add_messages
from langchain_community.tools import DuckDuckGoSearchRun
from langgraph.prebuilt import ToolNode, tools_condition
from PIL import Image
import requests
from io import BytesIO
import PyPDF2
import base64
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_openai import AzureChatOpenAI
from langchain_core.tools import tool
from dotenv import load_dotenv
import time
from langchain_community.tools import DuckDuckGoSearchRun
from langchain_community.utilities.duckduckgo_search import DuckDuckGoSearchAPIWrapper
from langchain_community.tools import BraveSearch
from tools.answer_question_from_file import AnswerQuestionFromFileTool
from tools.answer_question import AnswerQuestionTool
from tools.download_file import DownloadFile
from tools.reverse_string import ReverseString
from tools.web_search import WebSearchTool
from tools.wikipedia import WikipediaTool
from tools.youtube_transcript import YoutubeTranscriptTool
from tools.code_exec import PythonExecutionTool
from tools.code_gen import CodeGenTool
from tools.answer_excel import AnswerExcelTool
from contextlib import redirect_stdout
from tools.chess_tool import ChessTool
from tools.audio_tool import AudioTool

load_dotenv(".env", override=True)
BRAVE_API_KEY = os.getenv("BRAVE_API")

class State(TypedDict):
    file_path : str
    file: Optional[str]
    parsed_file: Optional[str]
    messages: Annotated[list[AnyMessage], add_messages]
    parsed_file_message: dict
    question: str
    response: str

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

# --- Basic Agent Definition ---
class BasicAgent:
    def __init__(self):

        # tools initialization
        #internet_search = DuckDuckGoSearchRun()
        
        tools = [CodeGenTool(), PythonExecutionTool(temp_dir="./"), YoutubeTranscriptTool(), 
                 AnswerQuestionFromFileTool(), AnswerQuestionTool(), DownloadFile(), 
                 ReverseString(), WebSearchTool(), WikipediaTool(), AnswerExcelTool(), ChessTool(), AudioTool()]

        llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash",
            temperature=0)
        self.llm_with_tools = llm.bind_tools(tools)

        builder = StateGraph(State)

        builder.add_node("assistant", self.assistant)
        builder.add_node("tools", ToolNode(tools))
        builder.add_node("final_answer", BasicAgent.final_answer)
        #builder.add_node("download_file", BasicAgent.download_file_node)
        #builder.add_node("parse_img", BasicAgent.parse_image)
        #builder.add_node("parse_pdf", BasicAgent.parse_pdf)
        #builder.add_node("parse_audio", BasicAgent.parse_audio)
        #builder.add_node("extract_data", BasicAgent.extract_data_from_file)

        builder.add_edge(START, "assistant")
        #builder.add_conditional_edges("download_file", BasicAgent.determine_file_type,
        #    {"img": "parse_img", "pdf": "parse_pdf", "audio": "parse_audio", "end": END})
        #builder.add_edge("parse_img", "assistant")
        #builder.add_edge("parse_pdf", "assistant")
        #builder.add_edge("parse_audio", "assistant")
        builder.add_conditional_edges(
            "assistant",
            tools_condition,
            path_map={
                "tools":   "tools",
                "__end__": "final_answer"
            }   
        )

        builder.add_edge("tools", "assistant")
        builder.add_edge("final_answer", END)

        self.react_graph = builder.compile()


    def __call__(self, question: str, file_name: Optional[str]) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        
        messages = [HumanMessage(question)]
        messages = self.react_graph.invoke({"messages": messages, "file_path": file_name, "question": question})

        with open(f'messages_{file_name}.txt', 'w', encoding='utf-8') as out:
            with redirect_stdout(out):
                for m in messages['messages']:
                    m.pretty_print()

        final_answer = messages["messages"][-1].content.strip()
        print(f"Final answer is {final_answer}")
        return final_answer


    def assistant(self, state: State):
        if state["file_path"]:
            file_name = state["file_path"].split(".")[0]
            file_extension = state["file_path"].split(".")[1]
        else:
            file_extension = None
            file_name = None
        
        prompt = f"""
        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. 
        
        You should read the prompt thoroughly. For example, if they ask you for athletes with the least number of athletes, you must be careful to what they ask (in case of tie, the country which is the first in alphabetical order.)

        You MUST ALWAYS PICK WIKIPEDIA TOOL BEFORE WEB SEARCH.
        
        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 SHOULD **NEVER** MAKE ANY ASSUMPTION AND USE THE TOOLS PROVIDED!
        
        You are given this file: {file_name} with the extension: {file_extension}.
        If a file is provided, the FIRST thing you MUST do is call the download_file tool!!
        The format must be {DEFAULT_API_URL}/files/{file_name}
        DO NOT PASS THE EXTENSION!!
        """

        sys_msg = SystemMessage(content=prompt)
        
        time.sleep(5)
        return {"messages": [self.llm_with_tools.invoke([sys_msg] + state["messages"])]}

    def final_answer(state: State):
        system_prompt = f"""
        You will be given an answer and a question. You MUST remove EVERYTHING not needed from the answer and answer the question exactly. 
        That is if you are being asked the number of something, you must not return the thought process, but just the number X.

        You must be VERY CAREFUL!! Of what the question asks.
        For example if they ask you to give the full name of a city without abbreviations you should stick to it (for example, St. Petersburg should be Saint Petersburg).
        """

        human_prompt = f"""
        Question: {state['question']}

        Answer: {state['messages'][-1]}
        """

        human_msg = HumanMessage(content=human_prompt)

        sys_msg = SystemMessage(content=system_prompt)

        time.sleep(1)

        llm = ChatGoogleGenerativeAI(
            model="gemini-2.0-flash",
            temperature=0)

        response = llm.invoke([sys_msg, human_msg]) 

        return {"messages": state["messages"] + [response]}

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")
        #if task_id != "99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3":
        #    continue
        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)
            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)