Update app.py

app.py

@@ -37,17 +37,21 @@ def get_complementary_color(rgb_color):
     h, s, v = colorsys.rgb_to_hsv(*rgb_color)
     complementary_h = (h + 0.5) % 1.0
     r, g, b = colorsys.hsv_to_rgb(complementary_h, s, v)
-    return closest_color((r, g, b))
+    complementary_color = closest_color((r, g, b))
+    
+    # Get shades or tones of the complementary color
+    complementary_palette = get_monochromatic_palette(complementary_color)
+    
+    return complementary_color, complementary_palette
+    
 
-def get_monochromatic_color(rgb_color):
-    h, s, v = colorsys.rgb_to_hsv(*rgb_color)
-    light_h = h
-    dark_h = (h + 0.5) % 1.0
-    light_rgb = colorsys.hsv_to_rgb(light_h, s, v)
-    dark_rgb = colorsys.hsv_to_rgb(dark_h, s, v)
-    light_color = closest_color(light_rgb)
-    dark_color = closest_color(dark_rgb)
-    return light_color, dark_color
+def get_monochromatic_palette(color_name, num_shades=3):
+    rgb_color = mcolors.CSS4_COLORS[color_name]
+    r, g, b = mcolors.hex2color(rgb_color)
+    hsv_colors = [colorsys.rgb_to_hsv(r, g, b)]
+    for i in range(1, num_shades):
+        hsv_colors.append((hsv_colors[-1][0], hsv_colors[-1][1], hsv_colors[-1][2] * (1 - 0.2 * i)))
+    return [mcolors.hsv_to_hex(color) for color in hsv_colors]
 
 
 def get_outfit_recommendation(pred_class):
@@ -60,12 +64,17 @@ def get_outfit_recommendation(pred_class):
 
 def predict(image):
     pred_class, pred_idx, outputs = learn.predict(image)
-    pred_class = class_names[pred_idx]  # Convert index to class name
     dominant_color = get_dominant_color(image)
-    complementary_color = get_complementary_color(dominant_color)
-    mono_light_color, mono_dark_color = get_monochromatic_color(dominant_color)
+    complementary_color, complementary_palette = get_complementary_color(dominant_color)
     garment_recommendation = get_outfit_recommendation(pred_class)
-    return f"Complementary item: {garment_recommendation} in {complementary_color}. Monochromatic options: {mono_light_color} or {mono_dark_color}."
+    
+    output = f"For your {pred_class.lower()}, consider pairing with a {garment_recommendation} in {complementary_color}."
+    output += "\n"
+    output += f"Monochromatic options:"
+    for color in complementary_palette:
+        output += f" {color},"
+    output = output[:-1]  # Removes the last comma
+    output += "."
 
 def gradio_predict(image):
     if isinstance(image, np.ndarray):