| import os | |
| import io | |
| import pandas as pd | |
| import requests | |
| from openai import OpenAI | |
| class GaiaAgent: | |
| def __init__(self): | |
| self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) | |
| self.instructions = ( | |
| "You are an expert assistant solving GAIA benchmark questions. " | |
| "You analyze file contents (like CSV), reason step-by-step, and respond with a single factual answer." | |
| ) | |
| self.api_url = "https://agents-course-unit4-scoring.hf.space" | |
| def fetch_file_context(self, task_id: str) -> str: | |
| try: | |
| url = f"{self.api_url}/files/{task_id}" | |
| response = requests.get(url, timeout=10) | |
| response.raise_for_status() | |
| content_type = response.headers.get("Content-Type", "") | |
| if "text/csv" in content_type or url.endswith(".csv"): | |
| df = pd.read_csv(io.StringIO(response.text)) | |
| if df.shape[1] <= 15 and df.shape[0] <= 30: | |
| return f"CSV table preview:\n{df.to_markdown(index=False)}" | |
| else: | |
| return f"CSV summary: {df.shape[0]} rows, {df.shape[1]} columns.\nColumns: {', '.join(df.columns[:10])}" | |
| elif "application/json" in content_type: | |
| return f"JSON content:\n{response.text[:2000]}" | |
| elif "application/pdf" in content_type: | |
| return "[PDF detected. You may need to request OCR summary.]" | |
| elif "text/plain" in content_type: | |
| return f"File preview:\n{response.text[:2000]}" | |
| else: | |
| return f"[Unsupported file type: {content_type}]" | |
| except Exception as e: | |
| return f"[Error downloading or processing file: {e}]" | |
| def __call__(self, question: str, task_id: str = None) -> str: | |
| file_context = "" | |
| if task_id: | |
| file_context = self.fetch_file_context(task_id) | |
| if file_context: | |
| file_context = f"FILE CONTEXT:\n{file_context}\n" | |
| prompt = f"{self.instructions}\n\n{file_context}QUESTION:\n{question}\nANSWER:" | |
| response = self.client.chat.completions.create( | |
| model="gpt-4-turbo", | |
| messages=[ | |
| {"role": "system", "content": self.instructions}, | |
| {"role": "user", "content": prompt} | |
| ], | |
| temperature=0.0, | |
| ) | |
| return response.choices[0].message.content.strip() | |