Add application file

Xception_skin.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:757416452aeaa4b34d52724497de724c91a11fc8ef9aa47930b813330f476566
+size 275909808

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+import matplotlib.pyplot as plt
+import io
+import pandas as pd
+
+# 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(share=True)
+