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import os
import json
from openai import OpenAI
from dotenv import load_dotenv
load_dotenv()
class ImageDescriber:
"""
Class for generating descriptive metadata (tags, description, caption)
for an image using Hugging Face's inference endpoint via OpenAI client.
"""
def __init__(self):
# Read token from environment variable
api_key = os.getenv("HF_TOKEN_1")
if not api_key:
raise ValueError("Environment variable HF_TOKEN_1 is not set.")
# Initialize client
self.client = OpenAI(
base_url="https://router.huggingface.co/v1",
api_key=api_key,
)
# Model to use
self.model = "Qwen/Qwen3-VL-8B-Instruct:novita"
def describe_image(self, image_url: str) -> dict:
"""
Sends the image to the model and returns a structured dictionary:
{
"tags": [...],
"description": "...",
"caption": "..."
}
"""
# Prompt for structured output
prompt = """
Describe this image in the following exact format:
result: {
"tags": [list of tags related to the image],
"description": "a 10-line descriptive description for the image",
"caption": "a short description for the image"
}
"""
# Send request
completion = self.client.chat.completions.create(
model=self.model,
messages=[
{
"role": "user",
"content": [
{"type": "text", "text": prompt},
{"type": "image_url", "image_url": {"url": image_url}},
],
}
],
)
# Extract message text
message = completion.choices[0].message
text_content = message.content.strip()
# Try to extract JSON-like dict from model output
try:
start = text_content.index("{")
end = text_content.rindex("}") + 1
json_str = text_content[start:end]
result = json.loads(json_str)
except Exception as e:
raise ValueError(f"Failed to parse model output: {text_content}\nError: {e}")
return result
def main():
"""
Entry point: takes image URL as input and prints parsed description.
"""
describer = ImageDescriber()
result = describer.describe_image("https://userx2000-cloudzy-ai-challenge.hf.space/uploads/img_2_20251024_082115_102.jpeg")
print("\n✅ Extracted Result:\n")
print(json.dumps(result, indent=2))
if __name__ == "__main__":
main()
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