Create evaluation_runner.py
Browse files- evaluation_runner.py +60 -0
evaluation_runner.py
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import requests
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import pandas as pd
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from tqdm import tqdm
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class EvaluationRunner:
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API_URL = "https://agents-course-unit4-scoring.hf.space"
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def run_evaluation(self, agent, username: str, agent_code: str):
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"""Полный цикл оценки"""
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questions = self._fetch_questions()
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if isinstance(questions, str):
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return questions, 0, 0, None
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results = []
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answers = []
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for q in tqdm(questions, desc="Processing"):
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try:
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response = agent(q["question"], q["task_id"])
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answer = response.get("final_answer", "")
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answers.append({
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"task_id": q["task_id"],
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"submitted_answer": str(answer)[:500] # Лимит длины
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})
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results.append({
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"Question": q["question"][:100],
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"Your Answer": str(answer)[:100],
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"Status": "Processed"
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})
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except Exception as e:
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results.append({
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"Question": q["question"][:100],
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"Your Answer": f"Error: {str(e)}",
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"Status": "Failed"
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})
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submission_result = self._submit_answers(username, agent_code, answers)
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return submission_result, 0, len(questions), pd.DataFrame(results)
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def _fetch_questions(self):
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try:
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response = requests.get(f"{self.API_URL}/questions", timeout=30)
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return response.json()
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except Exception as e:
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return f"Failed to fetch questions: {str(e)}"
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def _submit_answers(self, username: str, agent_code: str, answers: list):
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try:
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response = requests.post(
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f"{self.API_URL}/submit",
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json={
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"username": username.strip(),
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"agent_code": agent_code.strip(),
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"answers": answers
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},
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timeout=60
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)
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return response.json().get("message", "Submitted successfully")
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except Exception as e:
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return f"Submission failed: {str(e)}"
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