Update app.py

app.py

@@ -1,51 +1,51 @@
-from flask import Flask, render_template, request
-from PIL import Image
-from io import BytesIO
-import base64
-from predict import predict_potato, predict_tomato
-from model import potato_model, tomato_model
-import torch
-
-app = Flask(__name__)
-
-# Load models
-potato_model.load_state_dict(torch.load("models\\potato_model_statedict__f.pth", map_location=torch.device('cpu')))
-tomato_model.load_state_dict(torch.load("models\\tomato_model_statedict__f.pth", map_location=torch.device('cpu')))
-
-# potato_model = torch.load("Models\\potato_model_statedict__f.pth", map_location=torch.device('cpu'))
-# potato_model.load_state_dict(torch.load("Models\\potato_model_statedict__f.pth", map_location=torch.device('cpu')))
-# tomato_model = torch.load("Models\\tomato_model_statedict__f.pth", map_location=torch.device('cpu'))
-# potato_model.load_state_dict(torch.load("Models\\tomato_model_statedict__f.pth", map_location=torch.device('cpu')))
-
-
-@app.route('/')
-def home():
-    # Default to potato model
-    return render_template('index.html', model_type='potato')
-
-@app.route('/predict', methods=['POST'])
-def predict():
-    # Get the selected model type
-    model_type = request.form['model_type']
-    
-    # Get the image file from the request
-    file = request.files['file']
-    
-    if model_type == 'tomato':
-        class_name, probability, image = predict_tomato(file, tomato_model)
-        background_image = r'static\\tomato_background.jpg'
-
-    else:
-        class_name, probability, image = predict_potato(file, potato_model)
-        background_image = r'static\\potato_background.webp'      
-    
-    # Convert image to base64 format
-    buffered = BytesIO()
-    image.save(buffered, format="JPEG")
-    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
-    
-    # Pass the base64 encoded image and background image to the frontend
-    return render_template('index.html', image=img_str, class_name=class_name, probability=f"{probability * 100:.2f}%", background_image=background_image)
-
-if __name__ == '__main__':
-    app.run(debug=True)
+from flask import Flask, render_template, request
+from PIL import Image
+from io import BytesIO
+import base64
+from predict import predict_potato, predict_tomato
+from model import potato_model, tomato_model
+import torch
+
+app = Flask(__name__)
+
+# Load models
+potato_model.load_state_dict(torch.load("models/potato_model_statedict__f.pth", map_location=torch.device('cpu')))
+tomato_model.load_state_dict(torch.load("models/tomato_model_statedict__f.pth", map_location=torch.device('cpu')))
+
+# potato_model = torch.load("Models\\potato_model_statedict__f.pth", map_location=torch.device('cpu'))
+# potato_model.load_state_dict(torch.load("Models\\potato_model_statedict__f.pth", map_location=torch.device('cpu')))
+# tomato_model = torch.load("Models\\tomato_model_statedict__f.pth", map_location=torch.device('cpu'))
+# potato_model.load_state_dict(torch.load("Models\\tomato_model_statedict__f.pth", map_location=torch.device('cpu')))
+
+
+@app.route('/')
+def home():
+    # Default to potato model
+    return render_template('index.html', model_type='potato')
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    # Get the selected model type
+    model_type = request.form['model_type']
+    
+    # Get the image file from the request
+    file = request.files['file']
+    
+    if model_type == 'tomato':
+        class_name, probability, image = predict_tomato(file, tomato_model)
+        background_image = r'static/tomato_background.jpg'
+
+    else:
+        class_name, probability, image = predict_potato(file, potato_model)
+        background_image = r'static/potato_background.webp'      
+    
+    # Convert image to base64 format
+    buffered = BytesIO()
+    image.save(buffered, format="JPEG")
+    img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+    
+    # Pass the base64 encoded image and background image to the frontend
+    return render_template('index.html', image=img_str, class_name=class_name, probability=f"{probability * 100:.2f}%", background_image=background_image)
+
+if __name__ == '__main__':
+    app.run(debug=True)