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import os |
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os.environ["TF_USE_LEGACY_KERAS"] = "1" |
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from keras.models import load_model |
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import streamlit as st |
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import numpy as np |
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from io import BytesIO |
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from PIL import Image |
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import tensorflow as tf |
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st.markdown( |
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""" |
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<style> |
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.reportview-container { |
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background: url('./bg.jpg'); |
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background-size: cover; |
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} |
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</style> |
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""", |
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unsafe_allow_html=True |
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) |
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st.markdown("# Bananas Maturity Classification ") |
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st.sidebar.markdown("# Main Page") |
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MODEL = load_model("./1") |
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CLASS_NAMES = ["Banana_G1", "Banana_G2", "Rotten"] |
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def read_file_as_image(data) -> np.ndarray: |
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image = np.array(Image.open(BytesIO(data))) |
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return image |
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def predict( |
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file, |
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): |
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image = read_file_as_image(file.read()) |
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shape = image.shape |
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img_batch = np.expand_dims(image, 0) |
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img_batch = tf.image.resize(img_batch, (256, 256)) |
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prediction = MODEL.predict(img_batch) |
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predicted_class = CLASS_NAMES[np.argmax(prediction[0])] |
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confidence = np.max(prediction[0]) |
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if predicted_class == "Banana_G2": |
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predicted_class = "Green Banana- not ripen" |
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elif predicted_class == "Banana_G1": |
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predicted_class = "Mature Banana -ripen" |
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else: |
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predicted_class = "Rotten Banana" |
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return { |
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'class': predicted_class, |
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'confidence': float(confidence) |
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} |
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st.write("Upload an image or capture one with your camera") |
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option = st.selectbox("Choose an option", ["Upload Image", "Capture Image"]) |
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if option == "Upload Image": |
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uploaded_file = st.file_uploader("Choose an image", type=["jpg", "jpeg", "png"]) |
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if uploaded_file is not None: |
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result = predict(uploaded_file) |
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predicted_class = result['class'] |
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confidence = result['confidence'] |
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if predicted_class == "Green Banana- not ripen": |
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color = 'green' |
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elif predicted_class == "Mature Banana -ripen": |
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color = 'yellow' |
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else: |
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color = 'red' |
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st.markdown( |
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f'<p style="color:{color}; font-size:24px;">Predicted class: {predicted_class}, Confidence: {confidence:.2f}</p>', |
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unsafe_allow_html=True) |
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