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import os |
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import io |
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import pandas as pd |
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import requests |
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from openai import OpenAI |
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SKIPPED_TASKS = { |
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"a1e91b78-d3d8-4675-bb8d-62741b4b68a6", |
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"cca530fc-4052-43b2-b130-b30968d8aa44", |
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"9d191bce-651d-4746-be2d-7ef8ecadb9c2", |
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"99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3", |
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"1f975693-876d-457b-a649-393859e79bf3" |
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} |
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class GaiaAgent: |
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def __init__(self): |
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self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) |
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self.instructions = ( |
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"You are a precise and logical assistant solving GAIA benchmark questions. " |
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"Use any context or data provided. Respond with only the final answer." |
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) |
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self.api_url = "https://agents-course-unit4-scoring.hf.space" |
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def analyze_csv(self, csv_text: str, question: str) -> str: |
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try: |
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df = pd.read_csv(io.StringIO(csv_text)) |
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q = question.lower() |
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if "total" in q and "food" in q and "not including drinks" in q: |
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food_items = df[df["category"].str.lower() == "food"] |
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return f"Total food sales: ${food_items["sales"].sum():.2f}" |
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return f"Sample row: {df.iloc[0].to_dict()}" |
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except Exception as e: |
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return f"[CSV parse failed: {e}]" |
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def fetch_file_context(self, task_id: str, question: str) -> str: |
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try: |
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url = f"{self.api_url}/files/{task_id}" |
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response = requests.get(url, timeout=10) |
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response.raise_for_status() |
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content_type = response.headers.get("Content-Type", "") |
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if "csv" in content_type or url.endswith(".csv"): |
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return self.analyze_csv(response.text, question) |
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elif "json" in content_type: |
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return f"JSON Preview: {response.text[:1000]}" |
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elif "text/plain" in content_type: |
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return f"Text Preview: {response.text[:1000]}" |
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elif "pdf" in content_type: |
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return "[PDF detected. OCR not supported.]" |
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else: |
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return f"[Unsupported file type: {content_type}]" |
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except Exception as e: |
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return f"[File error: {e}]" |
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def __call__(self, question: str, task_id: str = None) -> str: |
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if task_id in SKIPPED_TASKS: |
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return "SKIPPED" |
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file_fact = "" |
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if task_id: |
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file_fact = self.fetch_file_context(task_id, question) |
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file_fact = f"FILE CONTEXT:\n{file_fact}\n" |
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prompt = f"{self.instructions}\n\n{file_fact}QUESTION: {question}\nANSWER:" |
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try: |
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response = self.client.chat.completions.create( |
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model="gpt-4-turbo", |
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messages=[ |
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{"role": "system", "content": self.instructions}, |
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{"role": "user", "content": prompt} |
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], |
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temperature=0.0, |
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) |
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return response.choices[0].message.content.strip() |
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except Exception as e: |
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return f"[Agent error: {e}]" |
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