PS | Enhanced theme and basic front end changes

app.py

@@ -1,4 +1,5 @@
 import gradio as gr
+import os
 from src.face_texture import GetFaceTexture
 from src.face_symmetry import GetFaceSymmetry
 from src.face_demographics import GetFaceDemographics
@@ -12,21 +13,26 @@ def combined_fn(input_image):
     proportion_results = GetFaceProportions().main(input_image)
     return (*texture_results, *symmetry_results, demographics_results, *proportion_results)
 
+gigi_hadid = os.path.join(os.path.dirname(__file__), "data/gigi_hadid.webp")
 
 iface = gr.Interface(
     fn=combined_fn,
-    inputs=gr.inputs.Image(type="pil"),
+    inputs=gr.Image(type="pil", label="Upload Face Image", value=gigi_hadid),
     outputs=[
-        gr.outputs.Image(type="pil"),  # From GetFaceTexture
-        gr.outputs.Image(type="pil"),  # From GetFaceTexture
-        "text",  # From GetFaceTexture
-        gr.outputs.Image(type="pil"),  # From GetFaceSymmetry
-        "text",  # From GetFaceSymmetry
-        "text",  # From GetFaceDemographics
-        "text",  # From GetFaceProportions
-        "text",  # From GetFaceProportions
-        gr.outputs.Image(type="pil"),  # From GetFaceProportions
+        gr.Image(type="pil", label="Extracted face"),
+        gr.Image(type="pil", label="Extracted face texture"), 
+        "json",
+        gr.Image(type="pil", label="Face symmetry"),  
+        "json",
+        "json",
+        "json",
+        "json",
+        gr.Image(type="pil", label="Face landmarks"),
     ],
+    title="Advanced Facial Feature Detector",
+    description="A comprehensive tool for detailed face analysis. Please upload a clear face image for best results.",
+    theme=gr.themes.Soft(),
+    live=False,
 )
 
 iface.launch()

notebooks/facial_age_gender.ipynb

@@ -515,11 +515,6 @@
     "# Other\n",
     "- Dataset used to train model: https://talhassner.github.io/home/projects/Adience/Adience-data.html#agegender"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {

src/face_demographics.py

@@ -68,7 +68,7 @@ class GetFaceDemographics:
         output = model(**inputs)
         proba = output.logits.softmax(1)
         preds = proba.argmax(1)  
-        age_confidence_score = max(proba[0]).item()
+        age_confidence_score = round(max(proba[0]).item(), 2)
         age = id2label[int(preds)]
         return age, age_confidence_score
     
@@ -86,7 +86,7 @@ class GetFaceDemographics:
         output = model(**inputs)
         proba = output.logits.softmax(1)
         preds = proba.argmax(1)  
-        gender_confidence_score = max(proba[0]).item()
+        gender_confidence_score = round(max(proba[0]).item(), 2)
         gender = id2label[int(preds)]
         return gender, gender_confidence_score
 

src/face_proportions.py

@@ -107,7 +107,9 @@ class GetFaceProportions:
         gray_image = self.preprocess_image(image)
         shape, image = self.detect_face_landmarks(gray_image)
         golden_ratios = self.compute_golden_ratios(shape)
+        golden_ratios = {k: round(v, 2) for k, v in golden_ratios.items()}
         equal_ratios = self.compute_equal_ratios(shape)
+        equal_ratios = {k: round(v, 2) for k, v in equal_ratios.items()}
         image = PILImage.fromarray(image)        
         return golden_ratios, equal_ratios, image
         

src/face_symmetry.py

@@ -133,6 +133,7 @@ class GetFaceSymmetry:
 
         full_face = np.hstack((best_left_half, best_right_half))
         full_face = PILImage.fromarray(full_face)
+        best_face_data = {k: float(round(v, 2)) for k, v in best_face_data.items()}
         return full_face, best_face_data
 
 

src/face_texture.py

@@ -57,7 +57,7 @@ class GetFaceTexture:
         face_image = self.get_face(gray_image)
         lbp, std = self.get_face_texture(face_image)
         face_texture_image = self.postprocess_image(lbp)
-        return face_texture_image, face_image, std
+        return face_image, face_texture_image, {"Texture std": round(std, 2)}
 
 
 if __name__ == "__main__":