Upload 4 files

Dockerfile (1)

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+FROM python:3.11
+WORKDIR /app
+
+RUN apt-get update && apt-get install -y \
+    libgl1 \
+    libglib2.0-0 \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt .
+RUN pip install --no-cache-dir -r requirements.txt
+COPY . .
+EXPOSE 8000
+EXPOSE 7860 
+CMD ["python", "main.py"]

README (3).md

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+---
+title: SkinAnalysis
+emoji: 🔎
+colorFrom: blue
+colorTo: gray
+sdk: docker
+pinned: false
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

main.py

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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"
+}
+
+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")
+    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"],
+            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)

requirements (3).txt

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+opencv-python
+gradio
+mediapipe
+pandas
+scipy
+skin-tone-classifier
+numpy
+huggingface-hub