Create app.py

app.py

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+from tensorflow.keras.preprocessing.image import ImageDataGenerator
+from tensorflow.keras.applications import MobileNetV2
+from tensorflow.keras import layers, models
+
+# Image data generator with augmentation
+train_datagen = ImageDataGenerator(
+    rescale=1.0/255,
+    rotation_range=20,
+    width_shift_range=0.2,
+    height_shift_range=0.2,
+    shear_range=0.2,
+    zoom_range=0.2,
+    horizontal_flip=True,
+    fill_mode='nearest'
+)
+
+test_datagen = ImageDataGenerator(rescale=1.0/255)
+
+train_generator = train_datagen.flow_from_directory(
+    '/content/drive/MyDrive/Skin Disease Data/Split_smol/train',
+    target_size=(150, 150),
+    batch_size=32,
+    class_mode='categorical'
+)
+
+validation_generator = test_datagen.flow_from_directory(
+    '/content/drive/MyDrive/Skin Disease Data/Split_smol/val',
+    target_size=(150, 150),
+    batch_size=32,
+    class_mode='categorical'
+)
+
+# Model architecture
+base_model = MobileNetV2(weights='imagenet', include_top=False, input_shape=(150, 150, 3))
+base_model.trainable = False  # Freeze the base model
+
+model = models.Sequential([
+    base_model,
+    layers.GlobalAveragePooling2D(),
+    layers.Dense(128, activation='relu'),
+    layers.Dense(len(labels), activation='softmax')
+])
+
+model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
+
+# Training
+history = model.fit(
+    train_generator,
+    steps_per_epoch=train_generator.samples // 32,
+    validation_data=validation_generator,
+    validation_steps=validation_generator.samples // 32,
+    epochs=20
+)
+
+# Save the model
+model.save('skin_disease_model.h5')
+
+import gradio as gr
+import numpy as np
+import tensorflow as tf
+
+# Load your trained model
+model = tf.keras.models.load_model('/content/skin_disease_model.h5')  # Update with your model path
+
+# Class labels
+labels = ['Actinic Keratosis', 'Atopic Dermatitis', 'Benign Keratosis', 
+          'Dermatofibroma', 'Melanocytic nevus', 'Melanoma', 
+          'Squamous cell carcinoma', 'Tinea Ringworm candidiasis', 
+          'Vascular lesion']
+
+# Gradio function for prediction
+def predict_skin_disease(img):
+    try:
+        # Preprocess the image
+        img = np.array(img) / 255.0  # Normalize
+        img = tf.image.resize(img, (150, 150))  # Resize to match model input
+        img = np.expand_dims(img, axis=0)  # Add batch dimension
+
+        # Make prediction using the model
+        prediction = model.predict(img)
+
+        # Convert prediction to a dictionary of class labels and probabilities
+        return {labels[i]: float(prediction[0][i]) for i in range(len(labels))}
+
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio Interface
+gr_interface = gr.Interface(
+    fn=predict_skin_disease,
+    inputs=gr.Image(type="numpy", label="Upload an Image of Skin Lesion"),
+    outputs=gr.Label(num_top_classes=3),  # Display top 3 predictions
+    title="Skin Disease Classification",
+    description="Upload an image of a skin lesion to classify it into various skin diseases."
+)
+
+# Launch the Gradio interface
+gr_interface.launch()