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_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

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

# (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 = [BasicAgent.search_tool, BasicAgent.revert_string, BasicAgent.download_file_tool, BasicAgent.answer_question_tool_from_file]

        #llm = ChatOllama(model="llama3.2", temperature=0)
        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("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)
        builder.add_edge("tools", "assistant")

        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})

        for m in messages['messages']:
            m.pretty_print()

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

    def search_tool(query: str):
        """
        This function looks for the provided query online and gives you information about it.
        """

        search_tool = BraveSearch.from_api_key(api_key=BRAVE_API_KEY, search_kwargs={"count": 3})
        res = search_tool.run(query)

        return res


    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
        
        prompt = f"""
        You are a helpful assistant. 
        You have access to some optional documents. The file name of the file you have access is: {file_name} and it is a {file_extension} file. The DEFAULT_API_URL to fetch this file is {DEFAULT_API_URL}.
        If you need to fetch a file, call the download_file tool with exactly the filename in the format {DEFAULT_API_URL}/files/file_name or URL. Once you have the bytes back (and the Base64), continue.
        You need to answer the given question EXACTLY in the SPECIFIC WAY it is asked in the user question. DO NOT ADD ANYTHING NOT NEEDED IN THE ANSWER.")
        """

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

    def file_to_download_exists(state: State) -> ["download", "apply_tools"]:
        """
            This function checks whether there is a file that needs to be downloaded
        """
        return state["file_path"] != ""

    
    def download_file_tool(file_url: str) -> dict:
        """
            This tool downloads a file (image, pdf, etc.) given the name of the file. The url for the request will be composed in the function so ONLY the name of the file should be passed in.

            You may have to download a file in 2 different scenarios:
            - A file given already as part of the task. In this case the format of the url must be: {DEFAULT_API_URL}/files/{file_name} THE EXTENSION OF THE FILE MUST NOT(!!) BE INCLUDED!
            - A url retrieved from the internet in the format https://some_url. In that case, you simply need to provide the url of the file that needs to be retrieved.

            Args: 
                file_name: the name of the file to be retrieved

            Output:
                A tuple made of:
                1) The file in bytes
                2) The file in Base64 encoding
                3) The result of the call 
        """
        #task_id = file_.split(".")[0]
        #print("Downloading the file")

        response = requests.get(file_url)
        if response.status_code == 200:
            msg = "File downloaded successfully!!"
            print(msg)
            file = response.content
            b64_file = base64.b64encode(state["file"]).decode("utf-8")
        else:
            msg = "There was an error downloading the file."
            print(msg)
            file = None
            b64_file = None

        return {
            "bytes": file,
            "base64": b64_file,
            "status": response.status_code,
        }

    def determine_file_type(state: State) -> ["pdf", "img", "audio", "end"]:
        if state["file"] is None:
            return "end"
        
        file_extension = state["file_path"].split(".")[1]
        if file_extension in ["png", "jpg"]:
            return "img"
        elif file_extension == "pdf":
            return "pdf"
        elif file_extension in ["mp3", "wav"]:
            return "audio"
        
        return "end"    

    def answer_question_tool_from_file(question: str, encoded_file: str, file_extension: str) -> str:
        """
        This tool allows you to answer a question taking into account information that were provided inside a file.

        Args:
            The question that needs to be answered.
            The file from which you want to get some information.
            The file extension of the file that is being processed.
        """

        if file_extension in ["png", "jpg"]:
            message = {"type": "image_url", "image_url": f"data:image/png;base64,{encoded_file}"}
        elif file_extension == "pdf":
            message = {"type": "image_url",  # Assuming the LLM accepts PDF under this key, you might need to verify this
                    "image_url": f"data:application/pdf;base64,{encoded_file}"
                  }
        elif file_extension in ["mp3", "wav"]:
            message = {"type": "media", "data": encoded_file,  # Use base64 string directly
            "mime_type": "audio/mpeg",
        }

        message_local = HumanMessage(
            content=[
                {"type": "text", "text": question},
                message,
            ]
        )

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

        response = llm.invoke(message_local)

        return response

    
    def revert_string(input_str: str) -> str:
        """
        This function inverst the order of the characters within a sentence. It is particularly useful if you can't understand the content
        in any language.

        Args:
            input_str: the string to invert
        
        Returns:
            The inverted string
        """

        return input_str[::-1]



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")
        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)