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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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r = requests.get(url, 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): |
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try: |
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r = self.client.chat.completions.create( |
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model="gpt-4-turbo", |
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messages=[{"role": "user", "content": prompt}], |
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temperature=0, |
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timeout=40 |
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) |
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return r.choices[0].message.content.strip() |
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except Exception: |
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return "[ERROR: ask failed]" |
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def search_context(self, query): |
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try: |
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result = self.search_tool.run(query) |
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return result[:2000] if result else "[NO WEB RESULT]" |
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except Exception: |
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return "[WEB ERROR]" |
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def handle_file(self, content, ctype, question): |
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try: |
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if "image" in ctype: |
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b64 = base64.b64encode(content).decode("utf-8") |
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result = self.client.chat.completions.create( |
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model="gpt-4o", |
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messages=[ |
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{"role": "system", "content": "You're a chess assistant. Reply only with the best move in algebraic notation (e.g., Qd1#)."}, |
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{"role": "user", "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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return result.choices[0].message.content.strip() |
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if "audio" in ctype: |
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with open("/tmp/audio.mp3", "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("/tmp/audio.mp3", "rb")) |
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return result.text |
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if "excel" in ctype: |
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df = pd.read_excel(io.BytesIO(content), engine="openpyxl") |
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df.columns = [c.lower().strip() for c in df.columns] |
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food = df[df['category'].str.lower() == 'food'] if 'category' in df.columns else df |
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food['sales'] = pd.to_numeric(food.get('sales'), errors='coerce') |
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return f"${food['sales'].sum():.2f}" |
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return content.decode("utf-8", errors="ignore")[:3000] |
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except Exception as e: |
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return f"[FILE ERROR: {e}]" |
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def extract_commutativity_set(self, table_txt): |
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try: |
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rows = [r for r in table_txt.splitlines() if r.strip().startswith("|")] |
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head = rows[0].split("|")[2:-1] |
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table = {} |
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for row in rows[1:]: |
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parts = row.split("|")[1:-1] |
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table[parts[0]] = parts[1:] |
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s = set() |
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for a in head: |
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for b in head: |
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if table[a][head.index(b)] != table[b][head.index(a)]: |
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s |= {a, b} |
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return ", ".join(sorted(s)) |
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except Exception: |
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return "[COMMUTATIVE ERROR]" |
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def extract_ingredients(self, text): |
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try: |
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tokens = re.findall(r"[a-zA-Z]+(?:\s[a-zA-Z]+)?", text) |
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noise = {"add", "combine", "cook", "mixture", "until", "dash", "medium", "heat", "remove", "stir"} |
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filtered = [t.lower() for t in tokens if t.lower() not in noise and len(t.split()) <= 3] |
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return ", ".join(sorted(set(filtered))) |
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except Exception: |
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return text[:80] |
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def format_answer(self, answer, question): |
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q = question.lower() |
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raw = answer.strip().strip("\"'") |
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if "commutative" in q: |
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return self.extract_commutativity_set(question) |
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if "ingredient" in q: |
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return self.extract_ingredients(raw) |
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if "algebraic notation" in q: |
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m = re.search(r"[KQBNR]?[a-h]?[1-8]?x?[a-h][1-8][+#]?", raw) |
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return m.group(0) if m else raw |
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if "usd" in q: |
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m = re.search(r"\$?\d+(\.\d{2})", raw) |
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return f"${m.group()}" if m else "$0.00" |
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if "award number" in q: |
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m = re.search(r"80NSSC[0-9A-Z]+", raw) |
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return m.group(0) if m else raw |
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if "ioc" in q: |
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m = re.search(r"\b[A-Z]{3}\b", raw) |
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return m.group(0) if m else raw |
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if "first name" in q: |
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return raw.split()[0] |
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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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m = re.search(r"\d+", raw) |
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return m.group(0) if m else raw |
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def retry_fallback(self, question): |
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context = self.search_context(question) |
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prompt = f"""Answer concisely and factually: |
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Context: {context} |
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Question: {question}""" |
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return self.ask(prompt) |
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def __call__(self, question, task_id=None): |
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try: |
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content, ctype = self.fetch_file(task_id) if task_id else (None, None) |
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context = self.handle_file(content, ctype, question) if content else self.search_context(question) |
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prompt = f"""Use this context to answer: |
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{context} |
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Question: |
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{question} |
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Answer:""" |
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raw = self.ask(prompt) |
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if not raw or "[ERROR" in raw or "step execution failed" in raw: |
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retry = self.retry_fallback(question) |
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return self.format_answer(retry, question) |
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return self.format_answer(raw, question) |
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except Exception as e: |
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return f"[AGENT ERROR: {e}]" |
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