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import os
import requests
from openai import OpenAI
class GaiaAgent:
def __init__(self):
self.client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
self.instructions = (
"You are a highly skilled and concise research assistant solving GAIA benchmark questions.\n"
"Analyze attached files, extract relevant information, reason step-by-step internally,\n"
"and return only the final factual answer in the correct format. Avoid explanations."
)
self.api_url = "https://agents-course-unit4-scoring.hf.space"
def fetch_file_content(self, task_id: str) -> str:
try:
url = f"{self.api_url}/files/{task_id}"
response = requests.get(url, timeout=10)
response.raise_for_status()
content_type = response.headers.get("Content-Type", "")
if "text" in content_type or "csv" in content_type or "json" in content_type:
return response.text[:3000] # Truncate to 3000 chars
elif "application/pdf" in content_type:
return "[PDF detected. Summarize manually if needed.]"
elif "image" in content_type:
return "[Image detected. Describe the image if needed.]"
elif "audio" in content_type:
return "[Audio detected. Provide transcription if needed.]"
else:
return f"[Unsupported file type: {content_type}]"
except Exception as e:
return f"[Error fetching file: {e}]"
def __call__(self, question: str, task_id: str = None) -> str:
file_context = ""
if task_id:
file_context = self.fetch_file_content(task_id)
if file_context:
file_context = f"Attached File Context:\n{file_context}\n"
# Add scratchpad-like structure
prompt = (
f"{self.instructions}\n\n"
f"{file_context}"
f"Question: {question}\n"
f"Think step-by-step to extract relevant facts and solve the task.\n"
f"Final Answer (no explanation, just the answer):"
)
response = self.client.chat.completions.create(
model="gpt-4-turbo",
messages=[
{"role": "system", "content": self.instructions},
{"role": "user", "content": prompt}
],
temperature=0.0,
)
return response.choices[0].message.content.strip()
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