Upload app.py

app.py

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+import streamlit as st
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+model = tf.keras.models.load_model('model sequential improve.h5')
+
+# Custom function to load and predict label for the image
+def predict(img_rel_path):
+    # Import Image from the path with size of (300, 300)
+    img = Image.open(img_rel_path).resize((150, 150))
+
+    # Convert Image to a numpy array
+    img = np.array(img)
+
+    # Scaling the Image Array values between 0 and 1
+    img = img / 255.0
+
+    # Get the Predicted Label for the loaded Image
+    p = model.predict(img[np.newaxis, ...])
+
+    # Label array
+    labels = {0: 'baby', 1: 'kid', 2: 'young', 3: 'adult'}
+
+    predicted_class = labels[np.argmax(p[0], axis=-1)]
+
+    classes=[]
+    prob=[]
+    for i,j in enumerate (p[0],0):
+        classes.append(labels[i])
+        prob.append(round(j*100,2))
+
+    return predicted_class, classes, prob
+
+def main():
+    st.title("Face Detection")
+    uploaded_file = st.file_uploader("Choose a file")
+    if uploaded_file is not None:
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+        if st.button("Predict"):
+            class_, classes, prob = predict(uploaded_file)
+            st.write("Age:", class_)
+            st.write("Predict:")
+            for i in range(len(classes)):
+                st.write(f"{classes[i].upper()}: {prob[i]}%")
+
+if __name__ == "__main__":
+    main()