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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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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, context, question): |
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try: |
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response = 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 an expert assistant. Use the context to answer factually and precisely. Respond with only the final answer, without explanation."}, |
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{"role": "user", "content": f"Context:\n{context}\n\nQuestion:\n{question}\n\nAnswer:"} |
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], |
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temperature=0, |
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timeout=25 |
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) |
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return response.choices[0].message.content.strip() |
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except Exception as e: |
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return f"[ERROR: {e}]" |
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def extract_web_context(self, question): |
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try: |
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return self.search_tool.run(question)[:1500] |
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except: |
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return "" |
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def handle_file(self, content, content_type, question): |
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if not content: |
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return "" |
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if "image" in content_type: |
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image_b64 = base64.b64encode(content).decode("utf-8") |
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messages = [ |
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{"role": "system", "content": "You're a chess assistant. Return only the best move for Black in algebraic notation. No commentary."}, |
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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, timeout=25) |
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return response.choices[0].message.content.strip() |
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if "audio" in content_type or question.endswith(".mp3"): |
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try: |
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path = "/tmp/audio.mp3" |
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with open(path, "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(path, "rb")) |
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return result.text[:2000] |
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except: |
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return "" |
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if "excel" in content_type: |
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try: |
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df = pd.read_excel(io.BytesIO(content), engine="openpyxl") |
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df.columns = [c.lower() for c in df.columns] |
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df['sales'] = pd.to_numeric(df['sales'], errors='coerce') |
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df = df[df['category'].str.lower() == 'food'] |
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return f"${df['sales'].sum():.2f}" |
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except: |
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return "$0.00" |
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try: |
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return content.decode("utf-8")[:3000] |
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except: |
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return "" |
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def format_answer(self, raw, question): |
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q = question.lower() |
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raw = raw.strip().strip("\"'") |
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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", raw) |
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return match.group(1) if match else raw |
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if "vegetables" in q or "ingredients" in q: |
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tokens = re.findall(r"[a-zA-Z]+", raw.lower()) |
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ignored = {"extract", "juice", "pure", "vanilla", "sugar", "granulated", "fresh", "ripe", "pinch", "water", "whole", "cups", "salt"} |
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items = sorted(set(t for t in tokens if t not in ignored and len(t) > 2)) |
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return ", ".join(items) |
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if "commutative" in q: |
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items = sorted(set(re.findall(r"[abcde]", raw))) |
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return ", ".join(items) |
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if "first name" in q: |
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return raw.split()[0] |
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if "award number" in q: |
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match = re.search(r"80NSSC[0-9A-Z]+", raw) |
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return match.group(0) if match else raw |
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if "ioc country code" in q: |
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match = re.search(r"\b[A-Z]{3}\b", raw.upper()) |
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return match.group(0) if match else raw |
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if "page numbers" in q: |
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nums = sorted(set(re.findall(r"\d+", raw))) |
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return ", ".join(nums) |
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if "at bats" in q: |
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match = re.search(r"\b\d{3,4}\b", raw) |
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return match.group(0) if match else raw |
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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})?)", raw) |
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return f"${float(match.group(1)):.2f}" if match else "$0.00" |
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try: |
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return str(w2n.word_to_num(raw)) |
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except: |
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match = re.search(r"\d+", raw) |
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return match.group(0) if match else raw |
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def __call__(self, question, task_id=None): |
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file_bytes, file_type = (None, None) |
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if task_id: |
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file_bytes, file_type = self.fetch_file(task_id) |
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context = self.handle_file(file_bytes, file_type, question) if file_bytes else self.extract_web_context(question) |
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if not context.strip(): |
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prompt_map = { |
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"youtube": "transcript of video site:youtube.com", |
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"malko": "malko competition winner yugoslavia site:wikipedia.org", |
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"veterinarian": "equine veterinarian site:libretexts.org site:ck12.org" |
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} |
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for k, v in prompt_map.items(): |
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if k in question.lower(): |
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context = self.extract_web_context(v) |
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break |
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raw = self.ask(context, question) |
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return self.format_answer(raw, question) |
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