Agent V31 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 # agent_v43.py — Najlepsze cechy z V18–V34: precyzja, retry fallback, stabilność import os import re import io import base64 import requests import pandas as pd from word2number import w2n from openai import OpenAI from langchain_community.tools import DuckDuckGoSearchRun class GaiaAgent: def __init__(self): self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) self.api_url = "https://agents-course-unit4-scoring.hf.space" self.search_tool = DuckDuckGoSearchRun() def fetch_file(self, task_id): try: url = f"{self.api_url}/files/{task_id}" r = requests.get(url, timeout=10) r.raise_for_status() return r.content, r.headers.get("Content-Type", "") except: return None, None def ask(self, prompt): try: r = self.client.chat.completions.create( model="gpt-4-turbo", messages=[{"role": "user", "content": prompt}], temperature=0, timeout=30 ) return r.choices[0].message.content.strip() except: return "[ERROR: ask failed]" def search_context(self, query): try: result = self.search_tool.run(query) return result[:2000] if result else "[NO WEB RESULT]" except: return "[WEB ERROR]" def handle_file(self, content, ctype, question): try: if "image" in ctype: b64 = base64.b64encode(content).decode("utf-8") result = self.client.chat.completions.create( model="gpt-4o", messages=[ {"role": "system", "content": "You're a chess assistant. Reply only with the best move in algebraic notation (e.g., Qd1#)."}, {"role": "user", "content": [ {"type": "text", "text": question}, {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}} ]} ] ) return result.choices[0].message.content.strip() if "audio" in ctype: with open("/tmp/audio.mp3", "wb") as f: f.write(content) result = self.client.audio.transcriptions.create(model="whisper-1", file=open("/tmp/audio.mp3", "rb")) return result.text if "excel" in ctype: df = pd.read_excel(io.BytesIO(content), engine="openpyxl") df.columns = [c.lower().strip() for c in df.columns] if 'category' in df.columns and 'sales' in df.columns: df = df.dropna(subset=['category', 'sales']) df = df[df['category'].str.lower() == 'food'] df['sales'] = pd.to_numeric(df['sales'], errors='coerce') return f"${df['sales'].sum():.2f}" return "[NO FOOD SALES DATA]" return content.decode("utf-8", errors="ignore")[:3000] except Exception as e: