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.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+cnn_model.keras filter=lfs diff=lfs merge=lfs -text
+wallpaper.jpg filter=lfs diff=lfs merge=lfs -text

app.py

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+import streamlit as st
+import eda
+import prediction
+
+page = st.sidebar.selectbox('Pilih halaman ', ('EDA', 'Prediction'))
+
+if page == 'EDA':
+    eda.run()
+else:
+    prediction.run()

cnn_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:62415b31f388fd90f974e7dbddc76aa0c2b53757a6005ec5750d97d570d9d2bb
+size 492722299

eda.py

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+import streamlit as st
+import plotly.express as px
+from PIL import Image
+
+def run():
+    # Membuat judul
+    st.title('Rice Image Dataset')
+
+    # Membuat Sub Header
+    st.header('EDA Rice Image Dataset', divider='gray')
+
+    # Menambahkan Gambar
+    image = Image.open('wallpaper.jpg')
+    st.image(image, caption = 'Rice', channels='RGB')
+
+    # Menambahkan Divider
+    st.divider()
+
+    # Jenis - Jenis Beras
+    st.header('Jenis-Jenis Beras', divider='gray')
+    st.subheader('Arborio')
+    image = Image.open('Arborio.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Basmati')
+    image = Image.open('Basmati.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Ipsala')
+    image = Image.open('Ipsala.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Jasmine')
+    image = Image.open('Jasmine.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Karacadag')
+    image = Image.open('Karacadag.png')
+    st.image(image, channels='RGB')
+    st.divider()
+
+    # Tepi Gambar Beras
+    st.header('Tepi Gambar Beras', divider='gray')
+    st.subheader('Arborio')
+    image = Image.open('Tepi Gambar Arborio.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Basmati')
+    image = Image.open('Tepi Gambar Basmati.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Ipsala')
+    image = Image.open('Tepi Gambar Ipsala.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Jasmine')
+    image = Image.open('Tepi Gambar Jasmine.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Karacadag')
+    image = Image.open('Tepi Gambar Karacadag.png')
+    st.image(image, channels='RGB')
+    st.divider()
+
+    # Warna Dominan Beras
+    st.header('Warna Dominan Beras', divider='gray')
+    st.subheader('Arborio')
+    image = Image.open('Color Dominance Arborio.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Basmati')
+    image = Image.open('Color Dominance Basmati.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Ipsala')
+    image = Image.open('Color Dominance Ipsala.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Jasmine')
+    image = Image.open('Color Dominance Jasmine.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Karacadag')
+    image = Image.open('Color Dominance Karacadag.png')
+    st.image(image, channels='RGB')
+    st.divider()
+
+    # Color Channel RGB Beras
+    st.header('Warna Channel RGB Beras', divider='gray')
+    st.subheader('Arborio')
+    image = Image.open('Color Channels Arborio.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Basmati')
+    image = Image.open('Color Channels Basmati.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Ipsala')
+    image = Image.open('Color Channels Ipsala.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Jasmine')
+    image = Image.open('Color Channels Jasmine.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Karacadag')
+    image = Image.open('Color Channels Karacadag.png')
+    st.image(image, channels='RGB')
+    st.divider()
+
+
+    # Color Channel CYMK Beras
+    st.header('Warna Channel RGB Beras', divider='gray')
+    st.subheader('Arborio')
+    image = Image.open('CYMK Color Channels Arborio.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Basmati')
+    image = Image.open('CYMK Color Channels Basmati.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Ipsala')
+    image = Image.open('CYMK Color Channels Ipsala.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Jasmine')
+    image = Image.open('CYMK Color Channels Jasmine.png')
+    st.image(image, channels='RGB')
+
+    st.subheader('Karacadag')
+    image = Image.open('CYMK Color Channels Karacadag.png')
+    st.image(image, channels='RGB')
+    st.divider()
+
+if __name__ == '__main__':
+    run()

prediction.py

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+import streamlit as st
+from PIL import Image
+import numpy as np
+from tensorflow.keras.models import load_model
+from tensorflow.keras.preprocessing.image import img_to_array
+
+# Function to preprocess the uploaded image
+def preprocess_image(image):
+    img = image.resize((img_height, img_width))
+    img_array = img_to_array(img)
+    img_array = np.expand_dims(img_array, axis=0)
+    img_array /= 255.0
+    return img_array
+
+# Mapping numerical predictions to class labels
+class_labels = {0: "Arborio", 1: "Basmati", 2: "Ipsala", 3: "Jasmine", 4: "Karacadag"}
+
+# Main function to run the Streamlit app
+def main():
+    st.title("Rice Classifier")
+    st.write("Upload a picture of rice to predict its type..")
+
+    # File upload
+    uploaded_file = st.file_uploader("Select the rice image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption="Gambar yang diunggah", use_column_width=True)
+
+        # Load the model (once the image is uploaded)
+        model = load_model("my_model.keras")
+
+        # Preprocess and predict
+        img_array = preprocess_image(image)
+        prediction = model.predict(img_array)
+        predicted_class = np.argmax(prediction, axis=1)[0]
+
+        # Display prediction
+        st.write(f'Prediksi: {class_labels[predicted_class]}')
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+pandas
+numpy
+scikit.learn==1.3.0
+tensorflow==2.15.0
+tensorflow_hub==0.16.1