app.py

import os
import re
import yaml
import openai
import pathlib
import inspect
import requests
import tempfile
import gradio as gr
import pandas as pd

from pathlib import Path
from tabulate import tabulate
from typing import Union, Optional
from smolagents.tools import PipelineTool, Tool
from smolagents import OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool, PythonInterpreterTool, VisitWebpageTool


DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
SYSTEM_PROMPT = """
You are a helpful assistant tasked with answering questions using a set of tools. 
Now, 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.
Your answer should only start with "FINAL ANSWER: ", then follows with the answer. 
"""

#-------------------------------------------------
#                      TOOLS
#-------------------------------------------------

#SpeeachToText
class SpeechToTextTool(PipelineTool):
    """
    Transcribes an audio file to text using the OpenAI Whisper API.
    Only local file paths are supported.
    """

    default_checkpoint = "openai/whisper-1"
    name = "transcriber"
    description = (
        "This tool sends an audio file to OpenAI Whisper and returns the "
        "transcribed text."
    )
    inputs = {
        "audio": {
            "type": "string",
            "description": "Absolute or relative path to a local audio file."
        }
    }
    output_type = "string"

    def __call__(self, audio: str) -> str:
        """
        Allows the tool to be called like a regular function:
            text = SpeechToTextTool()(path_to_audio)
        """
        return self._transcribe(audio)

    @staticmethod
    def _transcribe(audio_path: str) -> str:
        """
        Validates the file path and sends the audio to OpenAI Whisper API.
        Returns the transcribed text.
        """
        if not isinstance(audio_path, str):
            raise TypeError("Parameter 'audio' must be a string with the file path.")

        path = Path(audio_path).expanduser().resolve()
        if not path.is_file():
            raise FileNotFoundError(f"No such audio file: {path}")

        with path.open("rb") as file:
            response = openai.audio.transcriptions.create(
                file=file,
                model="whisper-1",
                response_format="text"
            )

        return response 

# ExcelToText
class ExcelToTextTool(Tool):
    """Render an Excel worksheet as Markdown text."""

    name = "excel_to_text"
    description = (
        "Read an Excel file and return a Markdown table of the requested sheet. "
        "Accepts either the sheet name or the zero-based index."
    )

    inputs = {
        "excel_path": {
            "type": "string",
            "description": "Path to the Excel file (.xlsx / .xls).",
        },
        "sheet_name": {
            "type": "string",
            "description": (
                "Worksheet name or zero‑based index *as a string* (optional; default first sheet)."
            ),
            "nullable": True,
        },
    }

    output_type = "string"


    def forward(self, excel_path: str, sheet_name: Optional[str] = None) -> str:
        """Load *excel_path* and return the sheet as a Markdown table."""
        path = self._resolve_path(excel_path)
        if not path.exists():
            return f"Error: Excel file not found at {path}"

        try:
            sheet = self._parse_sheet_identifier(sheet_name)
            df = pd.read_excel(path, sheet_name=sheet)
            return self._render_dataframe(df)

        except Exception as exc:
            return f"Error reading Excel file: {exc}"

    def _resolve_path(self, path_str: str) -> pathlib.Path:
        return pathlib.Path(path_str).expanduser().resolve()

    def _parse_sheet_identifier(self, name: Optional[str]) -> Union[str, int]:
        if not name:
            return 0
        return int(name) if name.isdigit() else name

    def _render_dataframe(self, df: pd.DataFrame) -> str:
        if hasattr(df, "to_markdown"):
            return df.to_markdown(index=False)

        from tabulate import tabulate
        return tabulate(df, headers="keys", tablefmt="github", showindex=False)


#-------------------------------------------------
#               AGENT DEFINITION
#-------------------------------------------------
class BasicAgent:
    def __init__(self):

        #Define model
        self.model = OpenAIServerModel(
            model_id="gpt-4o-mini",
            api_base="https://api.openai.com/v1",
            api_key=os.getenv("OPENAI_API_KEY")
        )

        #Define prompts
        self.prompt_templates = {}
        with open("prompt_templates.yaml", 'r') as stream:
            self.prompt_templates = yaml.safe_load(stream)

        #Define agent
        self.agent = CodeAgent(
            model = self.model,
            tools=[
                DuckDuckGoSearchTool(),
                VisitWebpageTool(),
                PythonInterpreterTool(
                    authorized_imports=[
                        'pandas',
                        'numpy',
                        're'
                    ]
                ),
                #WikipediaSearchTool(), 
                #SpeechToTextTool(), 
                #ExcelToTextTool()
            ],
            max_steps = 6,
            verbosity_level = 1,
            grammar = None,
            planning_interval = None,
            name = None,
            description = None,
            #add_base_tools = True,
            #prompt_templates=self.prompt_templates,
            additional_authorized_imports=['pandas','numpy','csv','subprocess', 're']
        )
        print("BasicAgent initialized.")
    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        answer = self.agent.run(question)
        print(f"Agent returning answer: {answer}")
        return 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")

    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 = agent(question_text)
            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)