Update app.py

app.py

@@ -1,25 +1,25 @@
-#import libraries
-import streamlit as st
-from tensorflow.keras.models import load_model
-from PIL import Image
-import numpy as np
-import cv2
-model=load_model('date_fruit_colab.keras')
-def process_image(img):
-    img=img.resize((150,150))
-    img=np.array(img)
-    img=img/255.0
-    img=np.expand_dims(img,axis=0)
-    return img
-st.title("Date Fruit Classification:date_tree:")
-st.write("Pick a picture and let the model predict the type of date")
-file=st.file_uploader('Pick a picture',type=['jpg','jpeg','png','JPG'])
-if file is not None:
-    img=Image.open(file)
-    st.image(img,caption='loaded picture')
-    image= process_image(img)
-    prediction=model.predict(image)
-    predicted_class=np.argmax(prediction)
-    
-    class_names=['Ajwa', 'Galaxy', 'Medjool', 'Meneifi', 'Nabtat Ali', 'Rutab', 'Shaishe', 'Sokari', 'Sugaey']
-    st.write(class_names[predicted_class])
+#import libraries
+import streamlit as st
+from tensorflow.keras.models import load_model
+from PIL import Image
+import numpy as np
+import cv2
+model=load_model('src/date_fruit_colab.keras')
+def process_image(img):
+    img=img.resize((150,150))
+    img=np.array(img)
+    img=img/255.0
+    img=np.expand_dims(img,axis=0)
+    return img
+st.title("Date Fruit Classification:date_tree:")
+st.write("Pick a picture and let the model predict the type of date")
+file=st.file_uploader('Pick a picture',type=['jpg','jpeg','png','JPG'])
+if file is not None:
+    img=Image.open(file)
+    st.image(img,caption='loaded picture')
+    image= process_image(img)
+    prediction=model.predict(image)
+    predicted_class=np.argmax(prediction)
+    
+    class_names=['Ajwa', 'Galaxy', 'Medjool', 'Meneifi', 'Nabtat Ali', 'Rutab', 'Shaishe', 'Sokari', 'Sugaey']
+    st.write(class_names[predicted_class])