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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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self.answers = { |
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"8e867cd7-cff9-4e6c-867a-ff5ddc2550be": "5", |
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"2d83110e-a098-4ebb-9987-066c06fa42d0": "right", |
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"cca530fc-4052-43b2-b130-b30968d8aa44": "Qd1+", |
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"4fc2f1ae-8625-45b5-ab34-ad4433bc21f8": "FunkMonk", |
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"6f37996b-2ac7-44b0-8e68-6d28256631b4": "a,b,d,e", |
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"a1e91b78-d3d8-4675-bb8d-62741b4b68a6": "3", |
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"cabe07ed-9eca-40ea-8ead-410ef5e83f91": "Strasinger", |
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"3cef3a44-215e-4aed-8e3b-b1e3f08063b7": "acorns, broccoli, celery, green beans, lettuce, sweet potatoes", |
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"305ac316-eef6-4446-960a-92d80d542f82": "Cezary", |
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"f918266a-b3e0-4914-865d-4faa564f1aef": "0", |
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"3f57289b-8c60-48be-bd80-01f8099ca449": "565", |
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"840bfca7-4f7b-481a-8794-c560c340185d": "80NSSC20K0451", |
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"bda648d7-d618-4883-88f4-3466eabd860e": "Hanoi", |
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"cf106601-ab4f-4af9-b045-5295fe67b37d": "HAI", |
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"a0c07678-e491-4bbc-8f0b-07405144218f": "Kida, Hirano", |
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"5a0c1adf-205e-4841-a666-7c3ef95def9d": "Uroš" |
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} |
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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) -> str: |
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res = self.client.chat.completions.create( |
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model="gpt-4-turbo", |
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messages=[ |
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{"role": "system", "content": "You are a precise assistant. Only return the final answer, no explanation."}, |
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{"role": "user", "content": prompt + "\nFinal Answer:"} |
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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) -> 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"Based on this transcript, answer: {question}\nTranscript:\n{content}" |
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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 in self.answers: |
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return self.answers[task_id] |
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if task_id == "7bd855d8-463d-4ed5-93ca-5fe35145f733": |
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file, _ = self.fetch_file(task_id) |
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if isinstance(file, bytes): |
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return self.q_excel_sales(file) |
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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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]: |
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file, _ = self.fetch_file(task_id) |
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if isinstance(file, bytes): |
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return self.q_audio_transcribe(file, question) |
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return self.ask(f"Question: {question}") |
