Spaces:
Sleeping
Sleeping
streamlit app
Browse files
main.py
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# Run this file using Streamlit command: streamlit run main.py
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import streamlit as st
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import tensorflow as tf
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import numpy as np
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from PIL import Image
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import io
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class CoffeeLandClassifier:
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def __init__(self, model_path, class_labels):
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self.model = tf.keras.models.load_model(model_path)
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self.class_labels = class_labels
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def run(self):
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st.title("Coffee Land Classifier")
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# Create a file uploader widget
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uploaded_image = st.file_uploader("Please upload an image", type=["jpg", "jpeg", "png"])
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if uploaded_image is not None:
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# Load and preprocess the uploaded image
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img = Image.open(uploaded_image)
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img = img.resize((64, 64)) # Resize the image to match the model's input shape
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img = np.array(img)
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img = img.astype('float32') / 255.0
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img = np.expand_dims(img, axis=0)
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# Make a prediction
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predictions = self.model.predict(img)
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class_index = np.argmax(predictions)
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predicted_class = self.class_labels[class_index]
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# Display the uploaded image
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st.image(img[0])
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# Show the prediction result
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st.write(f"Prediction: {predicted_class}")
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st.write("Class Probabilities:")
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for i, prob in enumerate(predictions[0]):
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st.write(f"{self.class_labels[i]}: {prob * 100:.2f}%")
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def main():
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model_path = "model/model.h5"
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class_labels = ["Coffee Land", "Not Coffee Land"] # Class labels
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classifier = CoffeeLandClassifier(model_path, class_labels)
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classifier.run()
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if __name__ == "__main__":
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main()
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