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import os |
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import requests |
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import pandas as pd |
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import gradio as gr |
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import asyncio |
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import tempfile |
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from agent import ( |
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answer_question, |
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transcribe_audio, |
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extract_excel_total_food_sales |
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) |
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
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class GAIALlamaAgent: |
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def __call__(self, question: str, file) -> str: |
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if file: |
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ext = os.path.splitext(file.name)[1].lower() |
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if ext == ".mp3": |
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return transcribe_audio(file.name) |
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if ext in (".xls", ".xlsx"): |
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return extract_excel_total_food_sales(file.name) |
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return asyncio.run(answer_question(question)) |
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def run_and_submit_all(profile: gr.OAuthProfile | None): |
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if not profile or not profile.username: |
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return "Please log in to Hugging Face.", None |
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username = profile.username |
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space_id = os.getenv("SPACE_ID", "") |
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agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main" |
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try: |
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questions = requests.get(f"{DEFAULT_API_URL}/questions", timeout=15).json() |
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except Exception as e: |
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return f"Error fetching questions: {e}", None |
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agent = GAIALlamaAgent() |
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payload, log = [], [] |
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for q in questions: |
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ans = agent(q["question"], None) |
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payload.append({"task_id": q["task_id"], "submitted_answer": ans}) |
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log.append({"Task ID": q["task_id"], "Question": q["question"], "Answer": ans}) |
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try: |
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res = requests.post(f"{DEFAULT_API_URL}/submit", |
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json={"username": username, "agent_code": agent_code, "answers": payload}, |
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timeout=60).json() |
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status = f"✅ Score: {res.get('score')}% ({res.get('correct_count')}/{res.get('total_attempted')})" |
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except Exception as e: |
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status = f"❌ Submission failed: {e}" |
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return status, pd.DataFrame(log) |
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with gr.Blocks() as demo: |
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gr.Markdown("# GAIA Agent") |
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gr.LoginButton() |
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with gr.Row(): |
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question = gr.Textbox(label="Question") |
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file_in = gr.File(label="Optional file (.mp3, .xlsx)") |
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ask_btn = gr.Button("Ask") |
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output = gr.Textbox(label="Answer") |
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ask_btn.click(lambda q, f: GAIALlamaAgent()(q, f), inputs=[question, file_in], outputs=output) |
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gr.Markdown("## Or run full GAIA evaluation") |
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eval_btn = gr.Button("Run Evaluation & Submit") |
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status = gr.Textbox(label="Status", lines=4) |
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table = gr.DataFrame() |
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eval_btn.click(run_and_submit_all, outputs=[status, table]) |
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if __name__ == "__main__": |
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print("🔁 App starting") |
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if os.getenv("SPACE_ID"): |
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print("🔗 Space:", os.getenv("SPACE_ID")) |
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demo.launch(debug=True) |
