uploaded the main.py and the trained model

ResNet152V2.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:808a967828894055c50485d20212089f983333be723e3aeea2acd21becb0a5b2
+size 240580936

main.py

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+import streamlit as st
+from PIL import Image
+import tensorflow as tf
+import numpy as np
+import io
+
+# Load your trained model
+custom_objects = {'BatchNormalization': tf.keras.layers.BatchNormalization}
+# model = tf.keras.models.load_model('ResNet152V2.h5')
+
+
+# Define class labels of the animals
+class_labels = ['Butterfly', 'Cat', 'Cow', 'Dog', 'Hen']
+
+# Streamlit App
+st.title("Image Classification App")
+
+# Upload image through Streamlit interface
+uploaded_file = st.file_uploader("Choose an image...", type="jpg")
+#
+# if uploaded_file is not None:
+#     # Read the bytes of the uploaded file
+#     image_bytes = uploaded_file.read()
+#
+#     # Convert the bytes to a PIL Image
+#     image = Image.open(io.BytesIO(image_bytes))
+#     st.image(image, caption="Uploaded Image", use_column_width=True)
+#
+#     # Preprocess the image for the model
+#     image = image.resize((256, 256))  # Adjust size as needed
+#     image_array = tf.keras.preprocessing.image.img_to_array(image)
+#     image_array = np.expand_dims(image_array, axis=0)
+#     image_array /= 255.0  # Normalize the pixel values to be between 0 and 1
+#
+#     # Make predictions
+#     predictions = model.predict(image_array)
+#     predicted_class = np.argmax(predictions[0])
+#     confidence = predictions[0][predicted_class]
+#
+#     # Display the predicted class and confidence
+#     st.write("Prediction:")
+#     st.write(f"Class: {class_labels[predicted_class]}, Confidence: {confidence:.2f}")