Update app.py

app.py

@@ -1,21 +1,39 @@
 import streamlit as st
+import numpy as np 
 import tensorflow as tf
+from PIL import Image
 
-st.title("Skin Disease Classification App")
+@st.cache(allow_output_mutation=True)
+def load_model():
+    model = tf.keras.models.load_model(TvachaAI.h5') 
+    return model
 
-# Load model 
-model = tf.keras.models.load_model('TvachaAI.h5')
+model = load_model()
 
-# Create sidebar for uploading images
-st.sidebar.title("Upload Image")
-image = st.sidebar.file_uploader("Upload an image", type=['png', 'jpg'])  
+CLASSES = ["Eczema", "Melanoma", "Atopic Dermatitis", "Basal Cell Carcinoma", "Melanocytic Nevi", 
+           "Benign Keratosis-like Lesions", "Psoriasis", "Seborrheic Keratoses", 
+           "Fungal Infections", "Viral Infections"]
 
-if image is not None:
+st.title("Skin Disease Classification")
+
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file)  
+    image = image.resize((224,224))
+    img_array = tf.keras.preprocessing.image.img_to_array(image)
+    img_array = np.expand_dims(img_array, axis=0) 
+    
+    pred = model.predict(img_array)
+    score = tf.nn.softmax(pred[0])
+    
+    st.image(image, caption='Uploaded Image.', use_column_width=True)
     
-    # Process & predict
-    img = process_image(image)  
-    preds = model.predict(img)
+    st.write("")
+    st.write("Classifying...")
+    label = "{}".format(CLASSES[np.argmax(score)])
+    st.write(label)
+    st.write(f"Score: {100 * np.max(score):.2f}%")
     
-    # Display image & predictions        
-    st.image(img, use_column_width=True)
-    st.write(f"Prediction: {decode_predictions(preds)}")
+else:
+    st.write("Please upload an image file")