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import os |
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import re |
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import io |
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import base64 |
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import requests |
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import pandas as pd |
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from word2number import w2n |
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from openai import OpenAI |
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from langchain_community.tools import DuckDuckGoSearchRun |
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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.api_url = "https://agents-course-unit4-scoring.hf.space" |
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self.search_tool = DuckDuckGoSearchRun() |
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def fetch_file(self, task_id): |
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try: |
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url = f"{self.api_url}/files/{task_id}" |
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r = requests.get(url, timeout=10) |
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r.raise_for_status() |
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return r.content, r.headers.get("Content-Type", "") |
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except: |
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return None, None |
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def ask(self, prompt): |
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try: |
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r = self.client.chat.completions.create( |
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model="gpt-4-turbo", |
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messages=[{"role": "user", "content": prompt}], |
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temperature=0, |
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timeout=30 |
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) |
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return r.choices[0].message.content.strip() |
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except: |
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return "[ERROR: ask failed]" |
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def search_context(self, query): |
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try: |
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result = self.search_tool.run(query) |
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return result[:2000] if result else "[NO WEB RESULT]" |
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except: |
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return "[WEB ERROR]" |
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def handle_file(self, content, ctype, question): |
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try: |
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if "image" in ctype: |
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b64 = base64.b64encode(content).decode("utf-8") |
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result = self.client.chat.completions.create( |
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model="gpt-4o", |
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messages=[ |
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{"role": "system", "content": "You're a chess assistant. Reply only with the best move in algebraic notation (e.g., Qd1#)."}, |
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{"role": "user", "content": [ |
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{"type": "text", "text": question}, |
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{b64}"}} |
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]} |
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] |
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) |
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return result.choices[0].message.content.strip() |
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if "audio" in ctype: |
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with open("/tmp/audio.mp3", "wb") as f: |
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f.write(content) |
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result = self.client.audio.transcriptions.create(model="whisper-1", file=open("/tmp/audio.mp3", "rb")) |
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return result.text |
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if "excel" in ctype: |
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df = pd.read_excel(io.BytesIO(content), engine="openpyxl") |
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df.columns = [c.lower().strip() for c in df.columns] |
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if 'category' in df.columns and 'sales' in df.columns: |
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df = df.dropna(subset=['category', 'sales']) |
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df = df[df['category'].str.lower() == 'food'] |
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df['sales'] = pd.to_numeric(df['sales'], errors='coerce') |
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return f"${df['sales'].sum():.2f}" |
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return "[NO FOOD SALES DATA]" |
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return content.decode("utf-8", errors="ignore")[:3000] |
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except Exception as e: |
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