streamlit app

app.py

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