Update app.py
Browse files
app.py
CHANGED
@@ -1,34 +1,217 @@
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import os
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import
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import requests
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import
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import pandas as pd
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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class BasicAgent:
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def __init__(self):
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print("BasicAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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fixed_answer =
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print(f"Agent returning
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return fixed_answer
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"""
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Fetches all questions, runs the BasicAgent on them, submits all answers,
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and displays the results.
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"""
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# --- Determine HF Space Runtime URL and Repo URL ---
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space_id =
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if profile:
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username= f"{profile.username}"
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print(f"User logged in: {username}")
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else:
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print("User not logged in.")
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@@ -38,10 +221,9 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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questions_url = f"{api_url}/questions"
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submit_url = f"{api_url}/submit"
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# 1. Instantiate Agent ( modify this part to create your agent)
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try:
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agent = BasicAgent()
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except Exception as e:
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print(f"Error instantiating agent: {e}")
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return f"Error initializing agent: {e}", None
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# 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)
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response.raise_for_status()
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questions_data = response.json()
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if not questions_data:
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-
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print(f"Fetched {len(questions_data)} questions.")
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except requests.exceptions.RequestException as e:
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print(f"Error fetching questions: {e}")
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return f"Error fetching questions: {e}", None
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except
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print(f"Error decoding JSON response from questions endpoint: {e}")
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print(f"Response text: {response.text[:500]}")
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return f"Error decoding server response for questions: {e}", None
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except Exception as e:
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print(f"An unexpected error occurred fetching questions: {e}")
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return f"An unexpected error occurred fetching questions: {e}", None
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@@ -76,23 +258,62 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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for item in questions_data:
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task_id = item.get("task_id")
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question_text = item.get("question")
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if not task_id or question_text is None:
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print(f"Skipping item with missing task_id or question: {item}")
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continue
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try:
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submitted_answer = agent(
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answers_payload.append(
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if not answers_payload:
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print("Agent did not produce any answers to submit.")
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return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
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# 4. Prepare Submission
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submission_data = {
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status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
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print(status_update)
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@@ -133,7 +354,7 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
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print(status_message)
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results_df = pd.DataFrame(results_log)
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return status_message, results_df
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except Exception as e:
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status_message = f"An unexpected error occurred during submission: {e}"
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print(status_message)
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results_df = pd.DataFrame(results_log)
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gr.Markdown(
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"""
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**Instructions:**
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1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
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2. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
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3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
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-
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---
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**Disclaimers:**
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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).
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run_button = gr.Button("Run Evaluation & Submit All Answers")
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status_output = gr.Textbox(
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# Removed max_rows=10 from DataFrame constructor
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results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
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run_button.click(
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fn=run_and_submit_all,
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outputs=[status_output, results_table]
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)
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if __name__ == "__main__":
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print("\n" + "-"*30 + " App Starting " + "-"*30)
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# Check for SPACE_HOST and SPACE_ID at startup for information
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space_host_startup = os.getenv("SPACE_HOST")
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space_id_startup = os.getenv("SPACE_ID")
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if space_host_startup:
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print(f"✅ SPACE_HOST found: {space_host_startup}")
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else:
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print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
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if space_id_startup:
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print(f"✅ SPACE_ID found: {space_id_startup}")
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print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
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print(
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else:
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print(
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print("-"*(60 + len(" App Starting ")) + "\n")
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print("Launching Gradio Interface for Basic Agent Evaluation...")
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demo.launch(debug=True, share=False)
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"""app.py"""
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import os
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import re
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import pathlib
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import tempfile
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from pathlib import Path
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from typing import Union, Optional
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import openai
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import requests
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import gradio as gr
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import pandas as pd
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from tabulate import tabulate
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from smolagents import (
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OpenAIServerModel,
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DuckDuckGoSearchTool,
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CodeAgent,
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WikipediaSearchTool,
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)
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from smolagents.tools import PipelineTool, Tool
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# (Keep Constants as is)
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# --- Constants ---
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
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class SpeechToTextTool(PipelineTool):
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"""
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Transcribes an audio file to text using the OpenAI Whisper API.
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Only local file paths are supported.
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"""
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default_checkpoint = "openai/whisper-1" # purely informational here
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description = (
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"This tool sends an audio file to OpenAI Whisper and returns the "
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"transcribed text."
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)
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name = "transcriber"
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inputs = {
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"audio": {
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"type": "string",
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"description": "Absolute or relative path to a local audio file.",
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}
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}
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output_type = "string"
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# ──────────────────────────────────────────────────────────────────────────
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# Public interface
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# ──────────────────────────────────────────────────────────────────────────
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def __call__(self, audio: str) -> str:
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"""
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Convenience wrapper so the tool can be used like a regular function:
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text = SpeechToTextTool()(path_to_audio)
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"""
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return self._transcribe(audio)
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# ──────────────────────────────────────────────────────────────────────────
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# Internal helpers
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# ──────────────────────────────────────────────────────────────────────────
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@staticmethod
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def _transcribe(audio_path: str) -> str:
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# ----- validation ----------------------------------------------------
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if not isinstance(audio_path, str):
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raise TypeError(
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"Parameter 'audio' must be a string containing the file path."
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)
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path = Path(audio_path).expanduser().resolve()
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if not path.is_file():
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raise FileNotFoundError(f"No such audio file: {path}")
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# ----- API call ------------------------------------------------------
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with path.open("rb") as fp:
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response = openai.audio.transcriptions.create(
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file=fp,
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model="whisper-1", # currently the only Whisper model
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response_format="text", # returns plain text instead of JSON
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)
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# For response_format="text", `response` is already the raw transcript
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return response
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class ExcelToTextTool(Tool):
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"""Render an Excel worksheet as Markdown text."""
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# ------------------------------------------------------------------
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# Required smol‑agents metadata
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# ------------------------------------------------------------------
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name = "excel_to_text"
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description = (
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"Read an Excel file and return a Markdown table of the requested sheet. "
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"Accepts either the sheet name or the zero-based index."
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)
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inputs = {
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"excel_path": {
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"type": "string",
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"description": "Path to the Excel file (.xlsx / .xls).",
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},
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"sheet_name": {
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"type": "string",
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"description": (
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"Worksheet name or zero-based index *as a string* (optional; default first sheet)."
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),
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"nullable": True,
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},
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}
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output_type = "string"
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def forward(
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self,
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excel_path: str,
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sheet_name: Optional[str] = None,
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) -> str:
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"""Load *excel_path* and return the sheet as a Markdown table."""
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path = pathlib.Path(excel_path).expanduser().resolve()
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if not path.exists():
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return f"Error: Excel file not found at {path}"
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try:
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# Interpret sheet identifier -----------------------------------
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sheet: Union[str, int]
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if sheet_name is None or sheet_name == "":
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sheet = 0 # first sheet
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else:
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# If the user passed a numeric string (e.g. "1"), cast to int
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sheet = int(sheet_name) if sheet_name.isdigit() else sheet_name
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# Load worksheet ----------------------------------------------
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df = pd.read_excel(path, sheet_name=sheet)
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# Render to Markdown; fall back to tabulate if needed ---------
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if hasattr(pd.DataFrame, "to_markdown"):
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return df.to_markdown(index=False)
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return tabulate(df, headers="keys", tablefmt="github", showindex=False)
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except Exception as exc: # pylint: disable=broad-except
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return f"Error reading Excel file: {exc}"
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+
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+
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def download_file_if_any(base_api_url: str, task_id: str) -> str | None:
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"""
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Try GET /files/{task_id}.
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• On HTTP 200 → save to a temp dir and return local path.
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• On 404 → return None.
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• On other errors → raise so caller can log / handle.
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"""
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url = f"{base_api_url}/files/{task_id}"
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try:
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resp = requests.get(url, timeout=30)
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if resp.status_code == 404:
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return None # no file
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resp.raise_for_status() # raise on 4xx/5xx ≠ 404
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except requests.exceptions.HTTPError as e:
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# propagate non-404 errors (403, 500, …)
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raise e
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# ▸ Save bytes to a named file inside the system temp dir
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# Try to keep original extension from Content-Disposition if present.
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cdisp = resp.headers.get("content-disposition", "")
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filename = task_id # default base name
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if "filename=" in cdisp:
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m = re.search(r'filename="([^"]+)"', cdisp)
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if m:
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filename = m.group(1) # keep provided name
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tmp_dir = Path(tempfile.gettempdir()) / "gaia_files"
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tmp_dir.mkdir(exist_ok=True)
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file_path = tmp_dir / filename
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with open(file_path, "wb") as f:
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f.write(resp.content)
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return str(file_path)
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class BasicAgent:
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"""Basic Agent for the evaluation task."""
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def __init__(self):
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self.agent = CodeAgent(
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model=OpenAIServerModel(model_id="gpt-4o"),
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tools=[
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DuckDuckGoSearchTool(),
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WikipediaSearchTool(),
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SpeechToTextTool(),
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ExcelToTextTool(),
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],
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add_base_tools=True,
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additional_authorized_imports=["pandas", "numpy", "csv", "subprocess"],
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)
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print("BasicAgent initialized.")
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def __call__(self, question: str) -> str:
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print(f"Agent received question (first 50 chars): {question[:50]}...")
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fixed_answer = self.agent.run(question)
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print(f"Agent returning answer: {fixed_answer}")
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return fixed_answer
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def run_and_submit_all(profile: gr.OAuthProfile | None):
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"""
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Fetches all questions, runs the BasicAgent on them, submits all answers,
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and displays the results.
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"""
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# --- Determine HF Space Runtime URL and Repo URL ---
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space_id = "l3xv/Final_Assignment_Template"
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if profile:
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username = f"{profile.username}"
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print(f"User logged in: {username}")
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else:
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print("User not logged in.")
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questions_url = f"{api_url}/questions"
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submit_url = f"{api_url}/submit"
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try:
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agent = BasicAgent()
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226 |
+
except Exception as e: # pylint: disable=broad-except
|
227 |
print(f"Error instantiating agent: {e}")
|
228 |
return f"Error initializing agent: {e}", None
|
229 |
# 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)
|
|
|
237 |
response.raise_for_status()
|
238 |
questions_data = response.json()
|
239 |
if not questions_data:
|
240 |
+
print("Fetched questions list is empty.")
|
241 |
+
return "Fetched questions list is empty or invalid format.", None
|
242 |
print(f"Fetched {len(questions_data)} questions.")
|
243 |
+
except requests.exceptions.JSONDecodeError as e:
|
244 |
+
print(f"Error decoding JSON response from questions endpoint: {e}")
|
245 |
+
print(f"Response text: {response.text[:500]}")
|
246 |
+
return f"Error decoding server response for questions: {e}", None
|
247 |
except requests.exceptions.RequestException as e:
|
248 |
print(f"Error fetching questions: {e}")
|
249 |
return f"Error fetching questions: {e}", None
|
250 |
+
except Exception as e: # pylint: disable=broad-except
|
|
|
|
|
|
|
|
|
251 |
print(f"An unexpected error occurred fetching questions: {e}")
|
252 |
return f"An unexpected error occurred fetching questions: {e}", None
|
253 |
|
|
|
258 |
for item in questions_data:
|
259 |
task_id = item.get("task_id")
|
260 |
question_text = item.get("question")
|
261 |
+
|
262 |
+
# ----------fetch any attached file ----------
|
263 |
+
try:
|
264 |
+
file_path = download_file_if_any(api_url, task_id)
|
265 |
+
except Exception as e: # pylint: disable=broad-except
|
266 |
+
file_path = None
|
267 |
+
print(f"[file fetch error] {task_id}: {e}")
|
268 |
+
|
269 |
+
# ---------- Build the prompt sent to the agent ----------
|
270 |
+
if file_path:
|
271 |
+
q_for_agent = (
|
272 |
+
f"{question_text}\n\n"
|
273 |
+
f"---\n"
|
274 |
+
f"A file was downloaded for this task and saved locally at:\n"
|
275 |
+
f"{file_path}\n"
|
276 |
+
f"---\n\n"
|
277 |
+
)
|
278 |
+
else:
|
279 |
+
q_for_agent = question_text
|
280 |
+
|
281 |
if not task_id or question_text is None:
|
282 |
print(f"Skipping item with missing task_id or question: {item}")
|
283 |
continue
|
284 |
try:
|
285 |
+
submitted_answer = agent(q_for_agent)
|
286 |
+
answers_payload.append(
|
287 |
+
{"task_id": task_id, "submitted_answer": submitted_answer}
|
288 |
+
)
|
289 |
+
results_log.append(
|
290 |
+
{
|
291 |
+
"Task ID": task_id,
|
292 |
+
"Question": question_text,
|
293 |
+
"Submitted Answer": submitted_answer,
|
294 |
+
}
|
295 |
+
)
|
296 |
+
except Exception as e: # pylint: disable=broad-except
|
297 |
+
print(f"Error running agent on task {task_id}: {e}")
|
298 |
+
results_log.append(
|
299 |
+
{
|
300 |
+
"Task ID": task_id,
|
301 |
+
"Question": question_text,
|
302 |
+
"Submitted Answer": f"AGENT ERROR: {e}",
|
303 |
+
}
|
304 |
+
)
|
305 |
|
306 |
if not answers_payload:
|
307 |
print("Agent did not produce any answers to submit.")
|
308 |
return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
|
309 |
|
310 |
+
# 4. Prepare Submission
|
311 |
+
submission_data = {
|
312 |
+
"username": username.strip(),
|
313 |
+
"agent_code": agent_code,
|
314 |
+
"answers": answers_payload,
|
315 |
+
}
|
316 |
+
|
317 |
status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
|
318 |
print(status_update)
|
319 |
|
|
|
354 |
print(status_message)
|
355 |
results_df = pd.DataFrame(results_log)
|
356 |
return status_message, results_df
|
357 |
+
except Exception as e: # pylint: disable=broad-except
|
358 |
status_message = f"An unexpected error occurred during submission: {e}"
|
359 |
print(status_message)
|
360 |
results_df = pd.DataFrame(results_log)
|
|
|
367 |
gr.Markdown(
|
368 |
"""
|
369 |
**Instructions:**
|
|
|
370 |
1. Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
|
371 |
2. Log in to your Hugging Face account using the button below. This uses your HF username for submission.
|
372 |
3. Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
|
|
|
373 |
---
|
374 |
**Disclaimers:**
|
375 |
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).
|
|
|
381 |
|
382 |
run_button = gr.Button("Run Evaluation & Submit All Answers")
|
383 |
|
384 |
+
status_output = gr.Textbox(
|
385 |
+
label="Run Status / Submission Result", lines=5, interactive=False
|
386 |
+
)
|
387 |
# Removed max_rows=10 from DataFrame constructor
|
388 |
results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)
|
389 |
|
390 |
+
run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table])
|
|
|
|
|
|
|
391 |
|
392 |
if __name__ == "__main__":
|
393 |
+
print("\n" + "-" * 30 + " App Starting " + "-" * 30)
|
394 |
# Check for SPACE_HOST and SPACE_ID at startup for information
|
395 |
space_host_startup = os.getenv("SPACE_HOST")
|
396 |
+
space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup
|
397 |
|
398 |
if space_host_startup:
|
399 |
print(f"✅ SPACE_HOST found: {space_host_startup}")
|
|
|
401 |
else:
|
402 |
print("ℹ️ SPACE_HOST environment variable not found (running locally?).")
|
403 |
|
404 |
+
if space_id_startup: # Print repo URLs if SPACE_ID is found
|
405 |
print(f"✅ SPACE_ID found: {space_id_startup}")
|
406 |
print(f" Repo URL: https://huggingface.co/spaces/{space_id_startup}")
|
407 |
+
print(
|
408 |
+
f" Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main"
|
409 |
+
)
|
410 |
else:
|
411 |
+
print(
|
412 |
+
"ℹ️ SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined."
|
413 |
+
)
|
414 |
|
415 |
+
print("-" * (60 + len(" App Starting ")) + "\n")
|
416 |
|
417 |
print("Launching Gradio Interface for Basic Agent Evaluation...")
|
418 |
+
demo.launch(debug=True, share=False)
|