added app.py

app.py

@@ -0,0 +1,42 @@
+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}")