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Update app.py
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app.py
CHANGED
@@ -3,37 +3,24 @@ import pandas as pd
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import gradio as gr
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import logging
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import time
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from dotenv import load_dotenv # Import load_dotenv
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from settings import Settings
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from evaluator import Evaluator
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from runner import Runner
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# Load environment variables from .env file at the very beginning
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load_dotenv()
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# Explicitly set environment variables for Pydantic Settings to ensure they are picked up
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# This is a safeguard in case pydantic-settings doesn't automatically find them
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# in certain execution environments (e.g., Hugging Face Spaces)
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if os.getenv("USERNAME"):
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os.environ["USERNAME"] = os.getenv("USERNAME")
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if os.getenv("SPACE_ID"):
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os.environ["SPACE_ID"] = os.getenv("SPACE_ID")
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if os.getenv("GEMINI_API_KEY"):
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os.environ["GEMINI_API_KEY"] = os.getenv("GEMINI_API_KEY")
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# Configure logging
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logging.basicConfig(level=logging.INFO, force=True)
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logger = logging.getLogger(__name__)
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# Initialize settings, evaluator, and runner
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settings = Settings()
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evaluator = Evaluator(settings)
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runner = Runner(settings)
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LOGIN_MESSAGE = "Please Login to Hugging Face with the button."
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EMPTY_RESULTS_TABLE = pd.DataFrame(columns=['task_id', 'question', 'answer'])
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@@ -100,17 +87,15 @@ with gr.Blocks() as demo:
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gr.Markdown(
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"""
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**Instructions:**
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1. Log in to your Hugging Face account using the button below.
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2. Click 'Get One Answer' to run the agent on a random question or 'Get All Answers' to run all.
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3. Click 'Submit Answers' to submit answers for evaluation. **Your HF username will be submitted for leaderboard tracking.**
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---
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**Disclaimers:**
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* Running 'Get All Answers' can take significant time as the agent processes all 20 questions.
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* Agent logs are detailed (DEBUG level) and may appear interleaved due to parallel execution.
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* The 'Submit Answers' button uses the most recent agent answers cached locally for your username.
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* **API Keys Required:** Ensure `GEMINI_API_KEY
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"""
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)
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@@ -158,5 +143,4 @@ if __name__ == "__main__":
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logger.info("-"*(60 + len(" App Starting ")) + "\n")
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logger.info("Launching Gradio Interface for GAIA Agent Evaluation...")
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demo.launch(debug=True, share=False)
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import gradio as gr
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import logging
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import time
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# Import the new Settings, Evaluator, and Runner classes
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from settings import Settings
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from evaluator import Evaluator
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from runner import Runner
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# Configure logging
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logging.basicConfig(level=logging.INFO, force=True)
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logger = logging.getLogger(__name__)
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# Initialize settings, evaluator, and runner
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settings = Settings()
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evaluator = Evaluator(settings)
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runner = Runner(settings)
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LOGIN_MESSAGE = "Please Login to Hugging Face with the button."
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EMPTY_RESULTS_TABLE = pd.DataFrame(columns=['task_id', 'question', 'answer'])
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gr.Markdown(
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"""
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**Instructions:**
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1. Log in to your Hugging Face account using the button below.
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2. Click 'Get One Answer' to run the agent on a random question or 'Get All Answers' to run all.
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3. Click 'Submit Answers' to submit answers for evaluation. **Your HF username will be submitted for leaderboard tracking.**
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---
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**Disclaimers:**
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* Running 'Get All Answers' can take significant time as the agent processes all 20 questions.
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* Agent logs are detailed (DEBUG level) and may appear interleaved due to parallel execution.
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* The 'Submit Answers' button uses the most recent agent answers cached locally for your username.
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* **API Keys Required:** Ensure `GEMINI_API_KEY` is set as a Space Secret (or environment variable if running locally).
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"""
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)
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logger.info("-"*(60 + len(" App Starting ")) + "\n")
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logger.info("Launching Gradio Interface for GAIA Agent Evaluation...")
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demo.launch(debug=True, share=False)
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