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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: |
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return None, None |
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def get_step_by_step_plan(self, 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=[{"role": "user", "content": f"Break down this question into 2–5 logical steps:\n{question}"}], |
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temperature=0, |
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timeout=15 |
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) |
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return response.choices[0].message.content.strip() |
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except: |
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return "Step 1: Try to understand the question." |
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def search_with_steps(self, question, steps): |
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try: |
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web_context = self.search_tool.run(question)[:2000] or "No useful info found." |
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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": f"Use only this data:\n{web_context}"}, |
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{"role": "user", "content": f"Follow this plan:\n{steps}\n\nAnswer this question concisely:\n{question}"} |
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], |
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temperature=0, |
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timeout=30 |
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) |
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return response.choices[0].message.content.strip() |
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except: |
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return "[ERROR: step execution failed]" |
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def handle_file(self, content, ctype, question): |
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if not content: |
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return "[NO FILE DATA]" |
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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. Respond with only the best move in algebraic notation."}, |
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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[:2000] |
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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.strip().lower() for c in df.columns] |
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if 'category' in df.columns and 'sales' in df.columns: |
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df = df.dropna(subset=['category', 'sales']) |
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df = df[df['category'].str.lower().str.strip() == 'food'] |
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df['sales'] = pd.to_numeric(df['sales'], errors='coerce') |
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return f"${df['sales'].sum():.2f}" |
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return "[NO FOOD SALES DATA]" |
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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_ingredients(self, text): |
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try: |
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items = re.findall(r"[a-zA-Z]+(?:\s[a-zA-Z]+)*", text.lower()) |
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blacklist = {'add', 'mix', 'cook', 'remove', 'combine', 'heat', 'stir', 'dash', 'before', 'cool', 'saucepan', 'until', 'mixture'} |
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filtered = [w for w in items if w not in blacklist and len(w.split()) <= 3] |
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unique = sorted(set(filtered)) |
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return ", ".join(unique[:15]) |
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except: |
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return text[:200] |
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def format_answer(self, raw, question): |
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if not raw: |
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return "[NO ANSWER]" |
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raw = raw.strip().strip("\"'") |
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q = question.lower() |
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if "ingredients" in q: |
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return self.extract_ingredients(raw) |
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if "algebraic notation" in q: |
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match = re.search(r"[KQBNR]?[a-h]?[1-8]?x?[a-h][1-8][+#]?", raw) |
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return match.group(0) if match else raw[:30] |
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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 "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 "first name" in q: |
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return raw.split()[0] if " " in raw else raw |
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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 __call__(self, question, task_id=None): |
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try: |
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file, ctype = self.fetch_file(task_id) if task_id else (None, None) |
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if file: |
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context = self.handle_file(file, ctype, question) |
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else: |
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steps = self.get_step_by_step_plan(question) |
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context = self.search_with_steps(question, steps) |
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return self.format_answer(context, question) |
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except Exception as e: |
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return f"[AGENT ERROR: {e}]" |
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