initial commit

cifar10_model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:0b85226c770950248e809ff1f6ee1bbe4167ba35ec05886ff63d1d0230b180a2
+size 2053808

main.py

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+import streamlit as st 
+import tensorflow as tf 
+import cv2
+from PIL import Image, ImageOps
+import numpy as np
+
+# st.set_option("deprecation.showfileUploaderEncoding", False)
+@st.cache(allow_output_mutation=True)
+
+def load_model():
+    model = tf.keras.models.load_model("F:/igebra/internship/ai ready/machine learning/image_classification_cnn/cifar10_model.h5")
+    return model
+
+model = load_model()
+
+st.title("CIFAR-10 Image Classification")
+uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+import cv2
+import numpy as np
+
+def import_and_predict(image_data, model):
+    size = (32, 32)
+    image = np.array(image_data)
+    image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB) if len(image.shape) > 2 else cv2.cvtColor(image, cv2.COLOR_GRAY2RGB)
+    image = cv2.resize(image, size, interpolation=cv2.INTER_AREA)
+    image = image / 255.0
+    img_reshape = np.expand_dims(image, axis=0)
+    prediction = model.predict(img_reshape)
+    return prediction
+
+
+if uploaded_file is None:
+    st.text("Please upload an image file")
+else:
+    image = Image.open(uploaded_file)
+    st.image(image, use_column_width=True)
+    predictions = import_and_predict(image, model)
+    print(predictions)
+    print(np.argmax(predictions))
+    classes = ["airplane", "automobile", "bird", "cat", "deer", "dog", "frog", "horse", "ship", "truck"] 
+    print(classes[np.argmax(predictions)])
+    string = ("This image is most likely is :")
+    st.success(f"This image most likely contains: {classes[np.argmax(predictions)]}")
+

requirments.txt

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+streamlit
+tensorflow
+cv2
+PIL
+numpy
+matplotlib