app.py

import os
import gradio as gr
import requests
import speech_recognition as sr
from smolagents import OpenAIServerModel, DuckDuckGoSearchTool, CodeAgent, WikipediaSearchTool
from pathlib import Path
import tempfile
from smolagents.tools import PipelineTool, Tool
import pathlib
from typing import Union, Optional
import pandas as pd
from tabulate import tabulate  # pragma: no cover – fallback path
import re
from transformers import AutoTokenizer, AutoModelForCausalLM
from langchain.agents import initialize_agent
from langchain_community.tools import DuckDuckGoSearchRun, WikipediaQueryRun
from langchain_community.llms import HuggingFaceHub

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


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"   # purely informational here
    description = (
        "This tool sends an audio file to OpenAI Whisper and returns the "
        "transcribed text."
    )
    name = "transcriber"
    inputs = {
        "audio": {
            "type": "string",
            "description": "Absolute or relative path to a local audio file.",
        }
    }
    output_type = "string"

    # ──────────────────────────────────────────────────────────────────────────
    # Public interface
    # ──────────────────────────────────────────────────────────────────────────
    def __call__(self, audio: str) -> str:
        """
        Convenience wrapper so the tool can be used like a regular function:
            text = SpeechToTextTool()(path_to_audio)
        """
        return self._transcribe(audio)

    # ──────────────────────────────────────────────────────────────────────────
    # Internal helpers
    # ──────────────────────────────────────────────────────────────────────────
    @staticmethod
    def _transcribe(audio_path: str) -> str:
        # ----- validation ----------------------------------------------------
        if not isinstance(audio_path, str):
            raise TypeError(
                "Parameter 'audio' must be a string containing the file path."
            )
        path = Path(audio_path).expanduser().resolve()
        if not path.is_file():
            raise FileNotFoundError(f"No such audio file: {path}")

        # ----- API call ------------------------------------------------------
        with path.open("rb") as fp:
            response = openai.audio.transcriptions.create(
                file=fp,
                model="whisper-1",        # currently the only Whisper model
                response_format="text"    # returns plain text instead of JSON
            )

        # For response_format="text", `response` is already the raw transcript
        return response

    def transcribe_audio(audio_file_path):
        recognizer = sr.Recognizer()
        with sr.AudioFile(audio_file_path) as source:
        audio_data = recognizer.record(source)
        try:
            text = recognizer.recognize_google(audio_data)
            return text
        except sr.UnknownValueError:
            return "Could not understand audio"
        except sr.RequestError:
            return "Could not request results (check internet connection)"


            

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

    # ------------------------------------------------------------------
    # Required smol‑agents metadata
    # ------------------------------------------------------------------
    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"

    # ------------------------------------------------------------------
    # Core logic
    # ------------------------------------------------------------------
    def forward(
            self,
            excel_path: str,
            sheet_name: Optional[str] = None,
    ) -> str:
        """Load *excel_path* and return the sheet as a Markdown table."""

        path = pathlib.Path(excel_path).expanduser().resolve()
        if not path.exists():
            return f"Error: Excel file not found at {path}"

        try:
            # Interpret sheet identifier -----------------------------------
            sheet: Union[str, int]
            if sheet_name is None or sheet_name == "":
                sheet = 0  # first sheet
            else:
                # If the user passed a numeric string (e.g. "1"), cast to int
                sheet = int(sheet_name) if sheet_name.isdigit() else sheet_name

            # Load worksheet ----------------------------------------------
            df = pd.read_excel(path, sheet_name=sheet)

            # Render to Markdown; fall back to tabulate if needed ---------
            if hasattr(pd.DataFrame, "to_markdown"):
                return df.to_markdown(index=False)
            from tabulate import tabulate  # pragma: no cover – fallback path

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

        except Exception as exc:  # broad catch keeps the agent chat‑friendly
            return f"Error reading Excel file: {exc}"


def download_file_if_any(base_api_url: str, task_id: str) -> str | None:
    """
    Try GET /files/{task_id}.
    • On HTTP 200 → save to a temp dir and return local path.
    • On 404 → return None.
    • On other errors → raise so caller can log / handle.
    """
    url = f"{base_api_url}/files/{task_id}"
    try:
        resp = requests.get(url, timeout=30)
        if resp.status_code == 404:
            return None          # no file
        resp.raise_for_status()   # raise on 4xx/5xx ≠ 404
    except requests.exceptions.HTTPError as e:
        # propagate non-404 errors (403, 500, …)
        raise e

    # ▸ Save bytes to a named file inside the system temp dir
    #    Try to keep original extension from Content-Disposition if present.
    cdisp = resp.headers.get("content-disposition", "")
    filename = task_id                                 # default base name
    if "filename=" in cdisp:
        m = re.search(r'filename="([^"]+)"', cdisp)
        if m:
            filename = m.group(1)                      # keep provided name

    tmp_dir = Path(tempfile.gettempdir()) / "gaia_files"
    tmp_dir.mkdir(exist_ok=True)
    file_path = tmp_dir / filename
    with open(file_path, "wb") as f:
        f.write(resp.content)
    return str(file_path)

# --- Basic Agent Definition ---
# ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------

class BasicAgent:
    def __init__(self):
        # Initialize LLM (requires HuggingFace API token)
        llm = HuggingFaceHub(
            repo_id="meta-llama/Meta-Llama-3-8B-Instruct"  #,
         #   huggingfacehub_api_token="your_token"
        )

        print("BasicAgent initialized.")

    def __call__(self, question: str) -> str:
        print(f"Agent received question (first 50 chars): {question[:50]}...")
        fixed_answer = self.agent.run(question)
        print(f"Agent returning answer: {fixed_answer}")
        return fixed_answer

        
        # Initialize tools
        tools = [
            DuckDuckGoSearchRun(),
            WikipediaQueryRun()
            # Would need custom implementations for other tools
        ]
        
        self.agent = initialize_agent(
            tools=tools,
            llm=llm,
            agent="zero-shot-react-description",
            verbose=True
        )

    def run(self, prompt):
        return self.agent.run(prompt)

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")
        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)