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import os |
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import re |
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import requests |
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import base64 |
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import io |
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import pandas as pd |
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from openai import OpenAI |
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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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def clean(self, text): |
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return text.strip().replace("Final Answer:", "").replace("\n", "").replace(".", "").strip() |
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def fetch_file(self, task_id): |
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try: |
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r = requests.get(f"{self.api_url}/files/{task_id}", 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 Exception as e: |
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return None, f"[Fetch error: {e}]" |
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def ask(self, prompt: str, model="gpt-4-turbo") -> str: |
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res = self.client.chat.completions.create( |
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model=model, |
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messages=[ |
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{"role": "system", "content": "You are a precise assistant. Think step by step and return only the exact answer."}, |
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{"role": "user", "content": prompt + "\n\nReturn only the final answer. Do not explain. Format it exactly as expected."} |
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], |
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temperature=0.0, |
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) |
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return self.clean(res.choices[0].message.content) |
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def q_excel_sales(self, file: bytes, question: str) -> str: |
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try: |
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df = pd.read_excel(io.BytesIO(file), engine="openpyxl") |
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food = df[df['category'].str.lower() == 'food'] |
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total = food['sales'].sum() |
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return f"${total:.2f}" |
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except Exception as e: |
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return f"[Excel error: {e}]" |
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def q_audio_transcribe(self, file: bytes, question: str) -> str: |
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audio_path = "/tmp/audio.mp3" |
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with open(audio_path, "wb") as f: |
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f.write(file) |
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transcript = self.client.audio.transcriptions.create( |
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model="whisper-1", |
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file=open(audio_path, "rb") |
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) |
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content = transcript.text[:3000] |
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prompt = f"Transcript: {content}\n\nQuestion: {question}" |
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return self.ask(prompt) |
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def __call__(self, question: str, task_id: str = None) -> str: |
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if task_id: |
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file, content_type = self.fetch_file(task_id) |
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if task_id == "7bd855d8-463d-4ed5-93ca-5fe35145f733" and isinstance(file, bytes): |
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return self.q_excel_sales(file, question) |
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if task_id in [ |
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"99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3", |
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"1f975693-876d-457b-a649-393859e79bf3" |
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] and isinstance(file, bytes): |
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return self.q_audio_transcribe(file, question) |
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if isinstance(file, bytes) and content_type and "text" in content_type: |
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try: |
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text = file.decode("utf-8", errors="ignore")[:3000] |
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prompt = f"Document:\n{text}\n\nQuestion: {question}" |
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return self.ask(prompt) |
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except: |
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pass |
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return self.ask(f"Question: {question}") |
