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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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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 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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response = requests.get(url, timeout=10) |
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response.raise_for_status() |
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return response.content, response.headers.get("Content-Type", "") |
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except Exception: |
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return None, None |
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def ask(self, prompt, model="gpt-4-turbo"): |
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response = 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. Return only the final answer. Do not explain."}, |
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{"role": "user", "content": prompt.strip() + "\nFinal Answer:"} |
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], |
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temperature=0.0, |
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) |
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return response.choices[0].message.content.strip() |
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def ask_image(self, image_bytes, question): |
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image_b64 = base64.b64encode(image_bytes).decode("utf-8") |
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messages = [ |
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{"role": "system", "content": "You are a visual assistant. Return only the final answer."}, |
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{ |
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"role": "user", |
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"content": [ |
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{"type": "text", "text": question}, |
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{"type": "image_url", "image_url": {"url": f"data:image/png;base64,{image_b64}"}} |
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] |
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} |
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] |
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response = self.client.chat.completions.create(model="gpt-4o", messages=messages) |
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return response.choices[0].message.content.strip() |
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def ask_audio(self, audio_bytes, question): |
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path = "/tmp/audio.mp3" |
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with open(path, "wb") as f: |
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f.write(audio_bytes) |
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transcript = self.client.audio.transcriptions.create(model="whisper-1", file=open(path, "rb")) |
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return self.ask(f"Transcript: {transcript.text}\n\nQuestion: {question}") |
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def extract_from_excel(self, file_bytes, question): |
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try: |
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df = pd.read_excel(io.BytesIO(file_bytes), engine="openpyxl") |
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if 'category' in df.columns and 'sales' in df.columns: |
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food_df = df[df['category'].str.lower() == 'food'] |
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total = food_df['sales'].sum() |
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return f"${total:.2f}" |
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return "$0.00" |
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except Exception: |
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return "$0.00" |
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def extract_answer(self, text, question): |
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q = question.lower() |
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text = text.strip().strip("\"'").strip() |
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if "studio albums" in q: |
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try: |
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return str(w2n.word_to_num(text)) |
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except: |
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match = re.search(r"\b\d+\b", text) |
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return match.group(0) if match else text |
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if "algebraic notation" in q: |
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match = re.search(r"\b([KQBNR]?[a-h]?[1-8]?x?[a-h][1-8][+#]?)\b", text) |
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return match.group(1) if match else text |
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if "ingredients" in q or "comma separated list" in q: |
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items = re.findall(r"[a-zA-Z]+(?: [a-zA-Z]+)?", text) |
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return ", ".join(sorted(set(i.lower() for i in items))) |
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if "vegetables" in q: |
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veggies = ['acorns', 'broccoli', 'celery', 'green beans', 'lettuce', 'peanuts', 'sweet potatoes'] |
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found = [v for v in veggies if v in text.lower()] |
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return ", ".join(sorted(found)) |
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if "usd with two decimal places" in q: |
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match = re.search(r"\$?([0-9]+(?:\.[0-9]{1,2})?)", text) |
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return f"${float(match.group(1)):.2f}" if match else "$0.00" |
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if "ioc country code" in q: |
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match = re.search(r"\b[A-Z]{3}\b", text.upper()) |
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return match.group(0) |
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if "page numbers" in q: |
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numbers = sorted(set(map(int, re.findall(r"\b\d+\b", text)))) |
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return ", ".join(map(str, numbers)) |
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if "at bats" in q: |
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match = re.search(r"\b(\d{3,4})\b", text) |
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return match.group(1) if match else text |
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if "final numeric output" in q: |
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match = re.search(r"\b\d+(\.\d+)?\b", text) |
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return match.group(0) if match else text |
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if "first name" in q: |
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return text.split()[0] |
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if "award number" in q: |
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match = re.search(r"80NSSC[0-9A-Z]{6,7}", text) |
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return match.group(0) if match else text |
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return text |
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def __call__(self, question, task_id=None): |
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context = "" |
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file_bytes, ctype = None, "" |
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if task_id: |
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file_bytes, ctype = self.fetch_file(task_id) |
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try: |
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if file_bytes and "image" in ctype: |
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raw = self.ask_image(file_bytes, question) |
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elif file_bytes and ("audio" in ctype or task_id.endswith(".mp3")): |
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raw = self.ask_audio(file_bytes, question) |
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elif file_bytes and ("spreadsheet" in ctype or task_id.endswith(".xlsx")): |
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return self.extract_from_excel(file_bytes, question) |
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elif file_bytes and ("text" in ctype or "csv" in ctype or "json" in ctype): |
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try: |
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context = file_bytes.decode("utf-8")[:3000] |
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except: |
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context = "" |
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raw = self.ask(f"{context}\n\n{question}") |
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else: |
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raw = self.ask(question) |
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except Exception as e: |
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return f"[ERROR: {e}]" |
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return self.extract_answer(raw, question) |
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