added wrinkle detection

main.py

@@ -12,7 +12,8 @@ PY_MODULES = {
     "inflammation.py": "RednessDetector",
     "texture.py": "TextureDetector",
     "skin_tone.py": "SkinToneDetector",
-    "oiliness.py": "OilinessDetector"  # Added oiliness module
+    "oiliness.py": "OilinessDetector", 
+    "wrinkle_unet.py": "WrinkleDetector"
 }
 
 def dynamic_import(module_path, class_name):
@@ -38,40 +39,47 @@ for py_file, class_name in PY_MODULES.items():
 # --- 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":
+    if analysis_type == "Dark Circles":
         detector = detector_classes["DarkCircleDetector"](image)
         result = detector.predict_json()
         output = detector.draw_json()
-    elif analysis_type == "redness":
+    elif analysis_type == "Redness":
         detector = detector_classes["RednessDetector"](image)
         result = detector.predict_json()
         output = result.get("overlay_image")
-    elif analysis_type == "texture":
+    elif analysis_type == "Texture":
         detector = detector_classes["TextureDetector"](image)
         result = detector.predict_json()
-        print(result)
+        # print(result)
         output = result.get("overlay_image")
-    elif analysis_type == "skin tone":
+    elif analysis_type == "Skin Tone":
         detector = detector_classes["SkinToneDetector"](image)
         result = detector.predict_json()
         output = result.get("output_image")
-    elif analysis_type == "oiliness":
+    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')}")
+        #     print(f"Oiliness scores: {result.get('scores')}")
+        # else:
+        #     print(f"Oiliness detection error: {result.get('error')}")
+    elif analysis_type == "Wrinkles":
+        detector = detector_classes["WrinkleDetector"](image)
+        result = detector.predict_json()
+        if result.get("detected") is not None:
+            output = detector.draw_json(result)
+
     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
+            ["Dark Circles", "Redness", "Texture", "Skin Tone", "Oiliness", "Wrinkles"],  
             label="Select Skin Analysis Type"
         ),
     ],