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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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from word2number import w2n |
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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, raw: str, question: str) -> str: |
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text = raw.strip() |
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text = re.sub(r"Final Answer:\s*", "", text, flags=re.IGNORECASE) |
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text = re.sub(r"Answer:\s*", "", text, flags=re.IGNORECASE) |
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text = text.strip().strip("\"'").strip() |
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if "studio albums" in question.lower(): |
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try: |
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return str(w2n.word_to_num(text.lower())) |
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except: |
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match = re.search(r"\b(\d+)\b", text) |
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return match.group(1) if match else text |
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if "algebraic notation" in question.lower(): |
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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 "comma separated list" in question.lower(): |
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words = re.findall(r"[a-zA-Z][a-zA-Z ]+[a-zA-Z]", text) |
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return ", ".join(sorted(set(w.strip().lower() for w in words))) |
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if "USD with two decimal places" in question: |
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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 question: |
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match = re.search(r"\b[A-Z]{3}\b", text.upper()) |
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return match.group(0) if match else text.upper() |
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if "page numbers" in question: |
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nums = sorted(set(map(int, re.findall(r"\b\d+\b", text)))) |
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return ", ".join(str(n) for n in nums) |
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if "at bats" in question.lower(): |
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match = re.search(r"(\d{3,4})", text) |
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return match.group(1) if match else text |
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if "final numeric output" in question: |
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match = re.search(r"(\d+(\.\d+)?)", text) |
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return match.group(1) if match else text |
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if "first name" in question.lower(): |
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return text.split()[0] |
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if "NASA award number" in question: |
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match = re.search(r"(80NSSC[0-9A-Z]{6,7})", text) |
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return match.group(1) if match else text |
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return text |
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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: |
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return None, None |
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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. Only return the final answer. Do not guess. Avoid hallucinations."}, |
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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 res.choices[0].message.content.strip() |
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def ask_image(self, image_bytes: bytes, question: str) -> str: |
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b64 = base64.b64encode(image_bytes).decode() |
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messages = [ |
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{"role": "system", "content": "You are a visual assistant. Return only the final answer. Do not guess."}, |
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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,{b64}"}} |
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] |
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} |
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] |
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res = self.client.chat.completions.create(model="gpt-4o", messages=messages) |
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return res.choices[0].message.content.strip() |
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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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if 'category' in df.columns and 'sales' in df.columns: |
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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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return "$0.00" |
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except Exception: |
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return "$0.00" |
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def q_audio_transcribe(self, file: bytes, question: str) -> str: |
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path = "/tmp/audio.mp3" |
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with open(path, "wb") as f: |
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f.write(file) |
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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 __call__(self, question: str, task_id: str = None) -> str: |
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context = "" |
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if task_id: |
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file, ctype = self.fetch_file(task_id) |
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if file and ctype: |
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if "image" in ctype: |
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return self.clean(self.ask_image(file, question), question) |
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if "audio" in ctype or task_id.endswith(".mp3"): |
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return self.clean(self.q_audio_transcribe(file, question), question) |
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if "spreadsheet" in ctype or "excel" in ctype or task_id.endswith(".xlsx"): |
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return self.clean(self.q_excel_sales(file), question) |
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if "text" in ctype: |
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try: |
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context += f"File Content:\n{file.decode('utf-8')[:3000]}\n" |
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except: |
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pass |
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return self.clean(self.ask(f"{context}\nQuestion: {question}"), question) |
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