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Upload app.py

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  1. app.py +57 -0
app.py ADDED
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+ from keras.models import load_model
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+ import keras.utils as image
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+ import numpy as np
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+ import cv2
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+ import tempfile
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+ import streamlit as st
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+ from PIL import Image
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+
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+ # Load the saved model
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+
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+
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+ # Load and preprocess an image for prediction
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+ # img_path = r'D:\PycharmProjects\hocmay\Dog_Cat_CNN2\anh-cho-cuoi.jpg' # Replace with the path to your image
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+ # Normalize the image
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+
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+
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+ # Perform prediction
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+ # Get the index of the predicted class
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+
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+
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+ model_file="model4.h5"
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+ img_file=st.file_uploader("Tải lên ảnh lớp",type=["png","jpg","jpeg"])
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+ temp_file2 = tempfile.NamedTemporaryFile(suffix=".pkl", delete=False)
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+ if img_file is not None:
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+ temp_file2.write(img_file.read())
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+
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+ #Loaded model
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+ loaded_model = load_model(model_file)
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+
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+ button2 = st.button("Xử lí", key="btn2")
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+ if button2:
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+ img = image.load_img(temp_file2.name, target_size=(128, 128))
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+ img_array = image.img_to_array(img)
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+ img_array = np.expand_dims(img_array, axis=0)
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+ img_array /= 255.0
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+
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+ prediction = loaded_model.predict(img_array)
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+ class_index = np.argmax(prediction)
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+
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+ if class_index == 0:
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+ img_cv2 = cv2.imread(temp_file2.name)
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+
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+ img_cv2 = cv2.putText(img_cv2, 'Cat', (00, 70), cv2.FONT_HERSHEY_SIMPLEX,
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+ 3, (0, 0, 255), thickness=5)
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+
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+
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+ st.image(img_cv2, caption='Ảnh mèo',channels="BGR")
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+ st.markdown("Đây là ảnh mèo")
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+
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+ else:
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+ img_cv2 = cv2.imread(temp_file2.name)
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+
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+ img_cv2 = cv2.putText(img_cv2, 'Dog', (00, 70), cv2.FONT_HERSHEY_SIMPLEX,
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+ 3, (0, 0, 255), thickness=5)
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+ st.image(img_cv2, caption='Ảnh chó',channels="BGR")
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+ st.markdown("Đây là ảnh chó")
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+