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app.py

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+import streamlit as st
+from keras.models import load_model
+from PIL import Image, ImageOps
+import numpy as np
+
+
+model = load_model("keras_model.h5", compile=False)
+
+
+class_names = open("labels.txt", "r").readlines()
+
+
+def predict(image):
+    
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    # Preprocess 
+    image = ImageOps.fit(image, (224, 224), Image.LANCZOS)
+    image_array = np.asarray(image)
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+    data[0] = normalized_image_array
+    # Make prediction
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index].strip()
+    confidence_score = prediction[0][index]
+    return class_name, confidence_score
+
+
+st.title("Image Classification")
+
+uploaded_file = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+    class_name, confidence_score = predict(image)
+    st.write("Class:", class_name)
+    st.write("Confidence Score:", confidence_score)

requirements.txt

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+tensorflow