Commit Model Deployment

app.py

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+import streamlit as st
+import cv2
+import numpy as np
+import os
+import tensorflow as tf
+from keras.applications.xception import preprocess_input, decode_predictions
+from keras.applications.xception import Xception
+from keras.preprocessing.image import load_img, img_to_array
+from keras.models import load_model
+
+# Load the model 
+model = load_model('model.h5')  
+
+# Function to treshold 
+def Predict(value):
+    if value >= 0.5:
+        return "Car"
+    else:
+        return "Bike"
+
+# Function to preprocess the uploaded image
+def preprocess_image(image):
+    img = load_img(image, target_size=(200, 200))
+    img_array = img_to_array(img) / 255.0
+    img_resized = tf.image.resize(img_array, (200, 200))
+    img_batch = tf.expand_dims(img_resized, axis=0)
+    return img_batch
+
+# Function to predict the image
+def predict_image(image):
+    img_array = preprocess_image(image)
+    predictions = model.predict(img_array)
+    return predictions[0][0]
+
+def main():
+    st.title("Image Classification")
+
+    uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+    
+    if uploaded_image is not None:
+        st.image(uploaded_image, caption='Uploaded Image', use_column_width=True)
+        
+        # Save the uploaded image temporarily
+        temp_image_path = "temp_image.jpg"
+        with open(temp_image_path, 'wb') as f:
+            f.write(uploaded_image.read())
+        
+        # Perform prediction
+        prediction = predict_image(temp_image_path)
+        prediction_text = Predict(prediction)
+        st.write(f"Prediction: {prediction_text}")
+
+        # Remove the temporary image file
+        os.remove(temp_image_path)
+
+if __name__ == '__main__':
+    main()

model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1b3f08ae1c63792fa3dc03aed8d6bfaeba0e5f5a6497f274d23b7ee8d6682c5a
+size 86980992

requirements.txt

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+streamlit 
+opencv-python-headless
+tensorflow
+numpy