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import os
import logging
from pathlib import Path
# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
# This is a placeholder for actual Hugging Face model integration
# In a real implementation, you would import and use appropriate models
def process_image(file_path, analysis_type):
"""
Process an image using Hugging Face models.
Args:
file_path (str): Path to the uploaded image
analysis_type (str): Type of analysis to perform (caption, etc.)
Returns:
str: Result of image analysis
"""
logger.info(f"Processing image: {file_path} with analysis type: {analysis_type}")
# For demonstration purposes, return a mock caption
# In a real implementation, you would use a Hugging Face model here
if analysis_type == "caption":
# Mock image captioning result
caption = generate_mock_caption(file_path)
return caption
else:
return "Unsupported analysis type"
def generate_mock_caption(image_path):
"""
Generate a mock image caption.
In a real implementation, this would use a Hugging Face model like
Qwen/Qwen2.5-VL-7B-Instruct or similar for image captioning.
"""
# Get file size as a simple way to generate different captions
file_size = os.path.getsize(image_path)
# List of mock captions
captions = [
"A beautiful landscape with mountains and a lake under blue sky.",
"A person working at a desk with a computer and various office supplies.",
"A busy city street with pedestrians and vehicles during the day.",
"A close-up photograph of a flower with vibrant colors and intricate details.",
"A group of people gathered around a table for what appears to be a business meeting.",
"A modern architectural building with glass and steel elements."
]
# Select a caption based on file size
mock_caption = captions[file_size % len(captions)]
return f"{mock_caption}\n\nNote: This is a mock caption. In a real implementation, this would be generated by a vision-language model like Qwen/Qwen2.5-VL-7B-Instruct." |