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import os |
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import gradio as gr |
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import requests |
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import pandas as pd |
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from typing import Tuple, Optional |
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from retriever import EnAgent as RetrieverAgent |
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DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space" |
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class EnAgent: |
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def __init__(self): |
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self.retriever_agent = RetrieverAgent() |
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def __call__(self, question: str) -> str: |
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return self.retriever_agent.answer_question(question) |
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def run_and_submit_all(profile: gr.OAuthProfile | None) -> Tuple[str, Optional[pd.DataFrame]]: |
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if not profile: |
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return "❌ Please Login to Hugging Face with the button.", None |
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username = profile.username |
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space_id = os.getenv("SPACE_ID") |
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api_url = DEFAULT_API_URL |
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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 = EnAgent() |
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except Exception as e: |
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return f"❌ Error initializing agent: {str(e)}", None |
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try: |
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response = requests.get(questions_url, timeout=15) |
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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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return "❌ Fetched questions list is empty or invalid format.", None |
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except requests.exceptions.RequestException as e: |
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return f"❌ Error fetching questions: {str(e)}", None |
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except Exception as e: |
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return f"❌ Unexpected error fetching questions: {str(e)}", None |
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results_log = [] |
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answers_payload = [] |
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for item in questions_data: |
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task_id = item.get("task_id") or item.get("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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continue |
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try: |
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submitted_answer = agent(question_text) |
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answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer}) |
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results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer}) |
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except Exception as e: |
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results_log.append({ |
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"Task ID": task_id, |
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"Question": question_text, |
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"Submitted Answer": f"AGENT ERROR: {str(e)}" |
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}) |
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if not answers_payload: |
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return "⚠️ Agent did not produce any answers to submit.", pd.DataFrame(results_log) |
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submission_data = { |
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"username": username.strip(), |
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"agent_code": f"https://huggingface.co/spaces/{space_id}/tree/main", |
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"answers": answers_payload |
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} |
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try: |
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response = requests.post(submit_url, json=submission_data, timeout=60) |
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response.raise_for_status() |
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result_data = response.json() |
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final_status = ( |
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f"✅ Submission Successful!\n" |
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f"User: {result_data.get('username')}\n" |
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f"Score: {result_data.get('score', 'N/A')}% " |
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f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n" |
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f"Message: {result_data.get('message', 'No message received.')}" |
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) |
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return final_status, pd.DataFrame(results_log) |
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except requests.exceptions.RequestException as e: |
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return f"❌ Submission Failed: {str(e)}", pd.DataFrame(results_log) |
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with gr.Blocks() as demo: |
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gr.Markdown("# En Agent") |
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gr.Markdown(""" |
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**Instructions:** |
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1. Log in to Hugging Face below. |
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2. Click the button to run your agent on questions and submit answers. |
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""") |
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gr.LoginButton() |
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run_button = gr.Button("Run Evaluation & Submit All Answers") |
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status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False) |
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results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True) |
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run_button.click(fn=run_and_submit_all, outputs=[status_output, results_table]) |
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if __name__ == "__main__": |
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demo.launch(debug=True, share=False) |
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