Update main.py

main.py

@@ -1,69 +1,67 @@
-from flask import Flask, request, render_template
-import numpy as np
-import os
-from PIL import Image
-import tensorflow as tf
-import os
-os.environ["SM_FRAMEWORK"] = "tf.keras"
-from tensorflow.keras.models import load_model
-import segmentation_models as sm
-from tensorflow.keras import backend as K
-import matplotlib.pyplot as plt
-
-app = Flask(__name__)
-app.config['UPLOAD_FOLDER'] = 'static/uploads'
-
-# Load your model
-weights = [0.1666] * 6  # Adjust if necessary
-dice_loss = sm.losses.DiceLoss(class_weights=weights)
-focal_loss = sm.losses.CategoricalFocalLoss()
-total_loss = dice_loss + (1 * focal_loss)
-
-def jacard_coef(y_true, y_pred):
-    y_true_f = K.flatten(y_true)
-    y_pred_f = K.flatten(y_pred)
-    intersection = K.sum(y_true_f * y_pred_f)
-    return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + 1.0)
-
-model_path = r"C:\Users\sriva\Videos\dubai segmentation\satellite_standard_unet_100epochs.hdf5"
-custom_objects = {
-    "dice_loss_plus_1focal_loss": total_loss,
-    "jacard_coef": jacard_coef
-}
-model = load_model(model_path, custom_objects=custom_objects)
-
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    if 'file' not in request.files:
-        return "No file uploaded.", 400
-    file = request.files['file']
-    if file.filename == '':
-        return "No selected file.", 400
-    
-    # Save the uploaded image
-    file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
-    file.save(file_path)
-
-    # Load and preprocess the image
-    test_img = Image.open(file_path)
-    desired_width = 256
-    desired_height = 256
-    test_img = test_img.resize((desired_width, desired_height))
-    test_img = np.array(test_img)
-    test_img_input = np.expand_dims(test_img, 0)
-
-    # Make the prediction
-    prediction = model.predict(test_img_input)
-    predicted_img = np.argmax(prediction, axis=3)[0, :, :]
-
-    # Save the predicted image
-    plt.imsave(os.path.join(app.config['UPLOAD_FOLDER'], 'predicted_' + file.filename), predicted_img, cmap='jet')
-
-    return render_template('index.html', uploaded_image=file.filename, predicted_image='predicted_' + file.filename)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, request, render_template
+import numpy as np
+import os
+from PIL import Image
+import tensorflow as tf
+import os
+os.environ["SM_FRAMEWORK"] = "tf.keras"
+from tensorflow.keras.models import load_model
+import segmentation_models as sm
+from tensorflow.keras import backend as K
+import matplotlib.pyplot as plt
+
+app = Flask(__name__)
+app.config['UPLOAD_FOLDER'] = 'static/uploads'
+
+# Load your model
+weights = [0.1666] * 6  # Adjust if necessary
+dice_loss = sm.losses.DiceLoss(class_weights=weights)
+focal_loss = sm.losses.CategoricalFocalLoss()
+total_loss = dice_loss + (1 * focal_loss)
+
+def jacard_coef(y_true, y_pred):
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersection = K.sum(y_true_f * y_pred_f)
+    return (intersection + 1.0) / (K.sum(y_true_f) + K.sum(y_pred_f) - intersection + 1.0)
+
+model_path = r"C:\Users\sriva\Videos\dubai segmentation\satellite_standard_unet_100epochs.hdf5"
+custom_objects = {
+    "dice_loss_plus_1focal_loss": total_loss,
+    "jacard_coef": jacard_coef
+}
+model = load_model(model_path, custom_objects=custom_objects)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    if 'file' not in request.files:
+        return "No file uploaded.", 400
+    file = request.files['file']
+    if file.filename == '':
+        return "No selected file.", 400
+    
+    # Save the uploaded image
+    file_path = os.path.join(app.config['UPLOAD_FOLDER'], file.filename)
+    file.save(file_path)
+
+    # Load and preprocess the image
+    test_img = Image.open(file_path)
+    desired_width = 256
+    desired_height = 256
+    test_img = test_img.resize((desired_width, desired_height))
+    test_img = np.array(test_img)
+    test_img_input = np.expand_dims(test_img, 0)
+
+    # Make the prediction
+    prediction = model.predict(test_img_input)
+    predicted_img = np.argmax(prediction, axis=3)[0, :, :]
+
+    # Save the predicted image
+    plt.imsave(os.path.join(app.config['UPLOAD_FOLDER'], 'predicted_' + file.filename), predicted_img, cmap='jet')
+
+    return render_template('index.html', uploaded_image=file.filename, predicted_image='predicted_' + file.filename)
+