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Agent V31
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# 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:
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