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import os |
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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 openai import OpenAI |
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TEXT_ONLY_TASKS = { |
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"2d83110e-a098-4ebb-9987-066c06fa42d0", |
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"4fc2f1ae-8625-45b5-ab34-ad4433bc21f8", |
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"6f37996b-2ac7-44b0-8e68-6d28256631b4", |
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"3cef3a44-215e-4aed-8e3b-b1e3f08063b7", |
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"305ac316-eef6-4446-960a-92d80d542f82", |
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"cf106601-ab4f-4af9-b045-5295fe67b37d", |
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"5a0c1adf-205e-4841-a666-7c3ef95def9d" |
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} |
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CSV_TASKS = { |
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"7bd855d8-463d-4ed5-93ca-5fe35145f733" |
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} |
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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.instructions = ( |
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"You are a precise assistant solving GAIA benchmark questions. " |
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"Only answer if you are confident you can provide the exact correct result." |
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) |
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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 as e: |
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return None, f"[FILE ERROR: {e}]" |
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def handle_csv_sales(self, csv_bytes): |
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try: |
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df = pd.read_excel(io.BytesIO(csv_bytes)) if csv_bytes[:4] == b"PK\x03\x04" else pd.read_csv(io.StringIO(csv_bytes.decode())) |
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if 'category' not in df.columns or 'sales' not in df.columns: |
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return "[MISSING COLUMN]" |
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food_df = df[df['category'].str.lower() == 'food'] |
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if food_df.empty: |
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return "[NO FOOD ITEMS FOUND]" |
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total = food_df['sales'].sum() |
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return f"${total:.2f}" |
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except Exception as e: |
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return f"[CSV ERROR: {e}]" |
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def __call__(self, question: str, task_id: str = None) -> str: |
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if task_id not in TEXT_ONLY_TASKS and task_id not in CSV_TASKS: |
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return "[SKIPPED: Task not eligible for high-confidence answer]" |
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if task_id in CSV_TASKS: |
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csv_bytes, err = self.fetch_file(task_id) |
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if csv_bytes: |
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result = self.handle_csv_sales(csv_bytes) |
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if result.startswith("["): |
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return "[SKIPPED: Confidence check failed]" |
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return result |
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return err |
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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": self.instructions}, |
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{"role": "user", "content": f"QUESTION: {question}\nANSWER (concise):"} |
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], |
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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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except Exception as e: |
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return f"[LLM ERROR: {e}]" |
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