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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 search_web_context(self, question): |
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try: |
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return self.search_tool.run(question) |
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except Exception: |
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return "[NO WEB INFO FOUND]" |
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def ask(self, context, question, model="gpt-4-turbo"): |
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messages = [ |
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{"role": "system", "content": "You are an expert assistant. Use provided web or file context to answer. Output only the short final answer, formatted correctly. Do not explain."}, |
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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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response = self.client.chat.completions.create( |
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model=model, |
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messages=messages, |
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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 format_answer(self, answer, question): |
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q = question.lower() |
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a = answer.strip().strip("\"'").strip() |
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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})?)", a) |
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return f"${float(match.group(1)):.2f}" if match else "$0.00" |
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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", a) |
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return match.group(1) if match else a |
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if "ioc country code" in q: |
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match = re.search(r"\b[A-Z]{3}\b", a.upper()) |
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return match.group(0) |
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if "first name" in q: |
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return a.split()[0] |
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if "page numbers" in q: |
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nums = sorted(set(re.findall(r"\b\d+\b", a))) |
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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", a) |
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return match.group(1) if match else a |
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if "studio albums" in q or "how many" in q: |
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try: |
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return str(w2n.word_to_num(a)) |
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except: |
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match = re.search(r"\b\d+\b", a) |
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return match.group(0) if match else a |
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if "award number" in q: |
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match = re.search(r"80NSSC[0-9A-Z]{6,7}", a) |
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return match.group(0) if match else a |
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if "vegetables" in q or "ingredients" in q: |
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tokens = [t.lower() for t in re.findall(r"[a-zA-Z]+", a)] |
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blacklist = {"extract", "juice", "pure", "vanilla", "sugar", "granulated", "fresh", "ripe", "pinch", "water", "whole", "cups", "salt"} |
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clean = sorted(set(t for t in tokens if t not in blacklist and len(t) > 2)) |
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return ", ".join(clean) |
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return a |
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def handle_file_context(self, file_bytes, ctype, question): |
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if not file_bytes: |
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return "" |
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if "image" in ctype: |
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image_b64 = base64.b64encode(file_bytes).decode("utf-8") |
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messages = [ |
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{"role": "system", "content": "You're a visual reasoning assistant. Answer the question based on the image. Output only the move in algebraic notation."}, |
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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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elif "audio" in ctype or question.endswith(".mp3"): |
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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_bytes) |
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transcript = self.client.audio.transcriptions.create(model="whisper-1", file=open(path, "rb")) |
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return transcript.text |
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elif "excel" in ctype or question.endswith(".xlsx"): |
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try: |
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df = pd.read_excel(io.BytesIO(file_bytes), 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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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}" if not pd.isna(total) else "$0.00" |
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except Exception: |
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return "[EXCEL ERROR]" |
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else: |
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try: |
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return file_bytes.decode("utf-8")[:3000] |
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except: |
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return "" |
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def __call__(self, question, task_id=None): |
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file_bytes, ctype = None, "" |
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if task_id: |
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file_bytes, ctype = self.fetch_file(task_id) |
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file_context = self.handle_file_context(file_bytes, ctype, question) |
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if file_context and not file_context.startswith("$"): |
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raw = self.ask(file_context, question) |
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elif file_context.startswith("$"): |
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return file_context |
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else: |
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web_context = self.search_web_context(question) |
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raw = self.ask(web_context, question) |
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return self.format_answer(raw, question) |
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