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load.py

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+from keras.models import load_model
+import keras.utils as image
+import numpy as np
+import cv2
+import tempfile
+import streamlit as st
+from PIL import Image
+
+# Load the saved model
+
+
+# Load and preprocess an image for prediction
+# img_path = r'D:\PycharmProjects\hocmay\Dog_Cat_CNN2\anh-cho-cuoi.jpg'  # Replace with the path to your image
+ # Normalize the image
+
+
+# Perform prediction
+  # Get the index of the predicted class
+
+
+model_file="model4.h5"
+img_file=st.file_uploader("Tải lên ảnh lớp",type=["png","jpg","jpeg"])
+temp_file2 = tempfile.NamedTemporaryFile(suffix=".pkl", delete=False)
+if img_file is not None:
+    temp_file2.write(img_file.read())
+
+    #Loaded model
+    loaded_model = load_model(model_file)
+
+    button2 = st.button("Xử lí", key="btn2")
+    if button2:
+        img = image.load_img(temp_file2.name, target_size=(128, 128))
+        img_array = image.img_to_array(img)
+        img_array = np.expand_dims(img_array, axis=0)
+        img_array /= 255.0
+
+        prediction = loaded_model.predict(img_array)
+        class_index = np.argmax(prediction)
+
+        if class_index == 0:
+            img_cv2 = cv2.imread(temp_file2.name)
+
+            img_cv2 = cv2.putText(img_cv2, 'Cat', (00, 70), cv2.FONT_HERSHEY_SIMPLEX,
+                                  3, (0, 0, 255), thickness=5)
+
+
+            st.image(img_cv2, caption='Ảnh mèo',channels="BGR")
+            st.markdown("Đây là ảnh mèo")
+
+        else:
+            img_cv2 = cv2.imread(temp_file2.name)
+
+            img_cv2 = cv2.putText(img_cv2, 'Dog', (00, 70), cv2.FONT_HERSHEY_SIMPLEX,
+                                  3, (0, 0, 255), thickness=5)
+            st.image(img_cv2, caption='Ảnh chó',channels="BGR")
+            st.markdown("Đây là ảnh chó")
+

model4.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1e49446e1160bcc52c42e1a92caa3352988f2a3959f47e15c070d43def2801c5
+size 103608144