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import gradio as gr
import tensorflow as tf
import numpy as np
# Load the Xception model
model = tf.keras.models.load_model("Xception_skin.h5") # Replace with your model path
# Define a function for inference and confidence values
def classify_image(input_image):
# Preprocess the image
img = input_image.resize((299, 299)) # Ensure the image size matches your Xception model's input size
img = np.array(img) / 255.0 # Normalize the image
img = np.expand_dims(img, axis=0) # Add batch dimension
# Perform classification
predictions = model.predict(img)
class_index = np.argmax(predictions[0]) # Get the index of the predicted class
confidence_values = predictions[0] # Get confidence values for all classes
# Map the class index to the corresponding class name using the provided dictionary
class_names = {
0: 'Eczema',
1: 'Warts Molluscum and other Viral Infections',
2: 'Melanoma',
3: 'Atopic Dermatitis',
4: 'Basal Cell Carcinoma (BCC)',
5: 'Melanocytic Nevi (NV) ',
6: 'Benign Keratosis-like Lesions (BKL)',
7: 'Psoriasis pictures Lichen Planus and related diseases',
8: 'Seborrheic Keratoses and other Benign Tumors',
9: 'Tinea Ringworm Candidiasis and other Fungal Infections'
}
confidences = {class_names[i]: float(confidence_values[i]) for i in range(len(confidence_values))}
#predicted_class_name = class_names[class_index] # Get the class name based on the index
return confidences
# Define the Gradio interface
input_image = gr.inputs.Image(type="pil")
#output_class_name = gr.outputs.Label(type='text', label='Predicted Class Name')
output_confidence_values = gr.outputs.Label(type='text', label='Confidence Values')
# Create the Gradio app
app = gr.Interface(
fn=classify_image,
inputs=input_image,
outputs=output_confidence_values,
title='Skin Disease Classifier',
description='Made By Roshan Rateria',
)
# Launch the Gradio app
app.launch()
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