Create app.py

app.py

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+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from tensorflow.keras.preprocessing import image
+
+# Load the trained model
+model = tf.keras.models.load_model("/content/drive/MyDrive/teeth_classification_model.h5")
+
+CLASS_NAMES = ['CaS', 'CoS', 'Gum', 'MC', 'OC', 'OLP', 'OT']
+
+st.title("Teeth Disease Classification")
+st.write("Upload an image to classify.")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
+
+def preprocess_image(img_path):
+    img = image.load_img(img_path, target_size=(224, 224))
+    img_array = image.img_to_array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+    return img_array
+
+if uploaded_file is not None:
+    img_path = "temp.jpg"
+    with open(img_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+
+    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
+
+    img_array = preprocess_image(img_path)
+
+    prediction = model.predict(img_array)
+    predicted_class = CLASS_NAMES[np.argmax(prediction)]
+
+    st.write(f"### Prediction: {predicted_class}")