Update app.py

app.py

@@ -1,3 +1,5 @@
+
+
 import gradio as gr
 import matplotlib.pyplot as plt
 import numpy as np
@@ -93,24 +95,23 @@ data_augmentation = keras.Sequential([
     layers.RandomFlip("horizontal", input_shape=(img_height, img_width, 3)),
     layers.RandomRotation(0.1),
     layers.RandomZoom(0.1),
-    layers.RandomContrast(0.1),
 ])
 
-# Load a pretrained model and fine-tune it
-base_model = tf.keras.applications.MobileNetV2(input_shape=(img_height, img_width, 3),
-                                               include_top=False,
-                                               weights='imagenet')
-base_model.trainable = False  # Freeze the base model
+num_classes = 12
 
-# Add custom layers on top of the pretrained model
 model = Sequential([
     data_augmentation,
     layers.Rescaling(1./255),
-    base_model,
-    layers.GlobalAveragePooling2D(),
+    layers.Conv2D(16, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(32, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
+    layers.Conv2D(64, 3, padding='same', activation='relu'),
+    layers.MaxPooling2D(),
     layers.Dropout(0.2),
+    layers.Flatten(),
     layers.Dense(128, activation='relu'),
-    layers.Dense(len(class_names), name="outputs")
+    layers.Dense(num_classes, name="outputs")
 ])
 
 model.compile(optimizer='adam',
@@ -126,19 +127,14 @@ history = model.fit(
 
 def predict_image(img):
     img = np.array(img)
-    img_resized = tf.image.resize(img, (img_height, img_width))
+    img_resized = tf.image.resize(img, (180, 180))
     img_4d = tf.expand_dims(img_resized, axis=0)
     prediction = model.predict(img_4d)[0]
-    max_index = np.argmax(prediction)
-    max_probability = tf.nn.softmax(prediction)[max_index]
-    
-    if max_probability == 1.0:
-        return {class_names[max_index]: float(max_probability)}
-    else:
-        return {"None": "No class with 100% confidence"}
+    probabilities = tf.nn.softmax(prediction).numpy()
+    return {class_names[i]: float(probabilities[i]) for i in range(len(class_names))}
 
 image = gr.Image()
-label = gr.Label(num_top_classes=1)
+label = gr.Label(num_top_classes=12)
 
 gr.Interface(
     fn=predict_image,