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import gradio as gr
import numpy as np
import cv2
import os
from huggingface_hub import hf_hub_download
import importlib.util
REPO_ID = "IFMedTech/Skin-Analysis"
# List of Python files and corresponding class names
PY_MODULES = {
"dark_circles.py": "DarkCircleDetector",
"inflammation.py": "RednessDetector",
"texture.py": "TextureDetector",
"skin_tone.py": "SkinToneDetector",
"oiliness.py": "OilinessDetector" # Added oiliness module
}
def dynamic_import(module_path, class_name):
spec = importlib.util.spec_from_file_location(class_name, module_path)
module = importlib.util.module_from_spec(spec)
spec.loader.exec_module(module)
return getattr(module, class_name)
# Dynamically download and import modules
detector_classes = {}
token = os.environ.get("HUGGINGFACE_TOKEN")
if not token:
raise ValueError("Please set the HUGGINGFACE_TOKEN environment variable in repo secrets!")
for py_file, class_name in PY_MODULES.items():
py_path = hf_hub_download(
repo_id=REPO_ID,
filename=py_file,
token=token
)
detector_classes[class_name] = dynamic_import(py_path, class_name)
# --- Skin analysis function using downloaded detectors ---
def analyze_skin(image: np.ndarray, analysis_type: str) -> np.ndarray:
output = image.copy()
if analysis_type == "dark circles":
detector = detector_classes["DarkCircleDetector"](image)
result = detector.predict_json()
output = detector.draw_json()
elif analysis_type == "redness":
detector = detector_classes["RednessDetector"](image)
result = detector.predict_json()
output = result.get("overlay_image")
elif analysis_type == "texture":
detector = detector_classes["TextureDetector"](image)
result = detector.predict_json()
print(result)
output = result.get("overlay_image")
elif analysis_type == "skin tone":
detector = detector_classes["SkinToneDetector"](image)
result = detector.predict_json()
output = result.get("output_image")
elif analysis_type == "oiliness":
detector = detector_classes["OilinessDetector"](image)
result = detector.predict_json()
if result.get("detected"):
output = result.get("overlay_image")
print(f"Oiliness scores: {result.get('scores')}")
else:
print(f"Oiliness detection error: {result.get('error')}")
return output
# --- Gradio Interface code ---
app = gr.Interface(
fn=analyze_skin,
inputs=[
gr.Image(type="numpy", label="Upload your face image"),
gr.Radio(
["dark circles", "redness", "texture", "skin tone", "oiliness"], # Added oiliness option
label="Select Skin Analysis Type"
),
],
outputs=gr.Image(type="numpy", label="Analyzed Image"),
title="Skin Analysis Demo",
description="Upload an image and choose a skin analysis parameter."
)
if __name__ == "__main__":
app.launch(server_name="0.0.0.0", server_port=7860)
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