Create app.py

app.py

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+from keras.models import load_model  # TensorFlow is required for Keras to work
+from PIL import Image, ImageOps  # Install pillow instead of PIL
+import numpy as np
+import streamlit as st
+
+# Disable scientific notation for clarity
+np.set_printoptions(suppress=True)
+
+# Load the model
+model = load_model("keras_model.h5", compile=False)
+
+# Load the labels
+class_names = open("labels.txt", "r").readlines()
+
+st.title("Cat Breed Identifier")
+st.header("Upload an Image to classify")
+
+uploaded_file = st.file_uploader("Choose the image...", type=['jpg','jpeg', 'png'])
+if uploaded_file is not None:
+    data = np.ndarray(shape=(1, 224, 224, 3), dtype=np.float32)
+    
+    # Replace this with the path to your image
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image...",use_column_width=True)
+    
+    # resizing the image to be at least 224x224 and then cropping from the center
+    size = (224, 224)
+    image = ImageOps.fit(image, size, Image.Resampling.LANCZOS)
+    
+    # turn the image into a numpy array
+    image_array = np.asarray(image)
+    
+    # Normalize the image
+    normalized_image_array = (image_array.astype(np.float32) / 127.5) - 1
+    
+    # Load the image into the array
+    data[0] = normalized_image_array
+    
+    # Predicts the model
+    prediction = model.predict(data)
+    index = np.argmax(prediction)
+    class_name = class_names[index]
+    confidence_score = prediction[0][index]
+    
+    # Print prediction and confidence score
+    st.write(f"Class: {class_name[2:].strip()}")
+    st.write(f"Confidence Score: {confidence_score}")