Update app.py

app.py

@@ -1,69 +1,69 @@
-import numpy as np
-import PIL.Image as Image
-import tensorflow as tf
-import streamlit as st
-from streamlit_extras.add_vertical_space import add_vertical_space
-from warnings import filterwarnings
-filterwarnings('ignore')
-
-
-def streamlit_config():
-
-    # page configuration
-    st.set_page_config(page_title='Classification', layout='centered')
-
-    # page header transparent color
-    page_background_color = """
-    <style>
-
-    [data-testid="stHeader"] 
-    {
-    background: rgba(0,0,0,0);
-    }
-
-    </style>
-    """
-    st.markdown(page_background_color, unsafe_allow_html=True)
-
-    # title and position
-    st.markdown(f'<h1 style="text-align: center;">Potato Disease Classification</h1>',
-                unsafe_allow_html=True)
-    add_vertical_space(4)
-
-
-# Streamlit Configuration Setup
-streamlit_config()
-
-
-def prediction(image_path, class_names=['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']):
-
-    img = Image.open(image_path)
-    img_resized = img.resize((256,256))
-    img_array = tf.keras.preprocessing.image.img_to_array(img_resized)
-    img_array = np.expand_dims(img_array, axis=0)
-
-    model = tf.keras.models.load_model(r'model\model.h5')
-    prediction = model.predict(img_array)
-
-    predicted_class = class_names[np.argmax(prediction)]
-    confidence = round(np.max(prediction)*100, 2)
-
-    add_vertical_space(1)
-    st.markdown(f'<h4 style="color: orange;">Predicted Class : {predicted_class}<br>Confident : {confidence}%</h3>', 
-                    unsafe_allow_html=True)
-    
-    add_vertical_space(1)
-    st.image(img.resize((400,300)))
-
-
-col1,col2,col3  = st.columns([0.1,0.9,0.1])
-with col2:
-    input_image = st.file_uploader(label='Upload the Image', type=['jpg', 'jpeg', 'png'])
-
-
-if input_image is not None:
-
-    col1,col2,col3 = st.columns([0.2,0.8,0.2])
-    with col2:
-        prediction(input_image)
-
+import numpy as np
+import PIL.Image as Image
+import tensorflow as tf
+import streamlit as st
+from streamlit_extras.add_vertical_space import add_vertical_space
+from warnings import filterwarnings
+filterwarnings('ignore')
+
+
+def streamlit_config():
+
+    # page configuration
+    st.set_page_config(page_title='Classification', layout='centered')
+
+    # page header transparent color
+    page_background_color = """
+    <style>
+
+    [data-testid="stHeader"] 
+    {
+    background: rgba(0,0,0,0);
+    }
+
+    </style>
+    """
+    st.markdown(page_background_color, unsafe_allow_html=True)
+
+    # title and position
+    st.markdown(f'<h1 style="text-align: center;">Potato Disease Classification</h1>',
+                unsafe_allow_html=True)
+    add_vertical_space(4)
+
+
+# Streamlit Configuration Setup
+streamlit_config()
+
+
+def prediction(image_path, class_names=['Potato___Early_blight', 'Potato___Late_blight', 'Potato___healthy']):
+
+    img = Image.open(image_path)
+    img_resized = img.resize((256,256))
+    img_array = tf.keras.preprocessing.image.img_to_array(img_resized)
+    img_array = np.expand_dims(img_array, axis=0)
+
+    model = tf.keras.models.load_model('model.h5')
+    prediction = model.predict(img_array)
+
+    predicted_class = class_names[np.argmax(prediction)]
+    confidence = round(np.max(prediction)*100, 2)
+
+    add_vertical_space(1)
+    st.markdown(f'<h4 style="color: orange;">Predicted Class : {predicted_class}<br>Confident : {confidence}%</h3>', 
+                    unsafe_allow_html=True)
+    
+    add_vertical_space(1)
+    st.image(img.resize((400,300)))
+
+
+col1,col2,col3  = st.columns([0.1,0.9,0.1])
+with col2:
+    input_image = st.file_uploader(label='Upload the Image', type=['jpg', 'jpeg', 'png'])
+
+
+if input_image is not None:
+
+    col1,col2,col3 = st.columns([0.2,0.8,0.2])
+    with col2:
+        prediction(input_image)
+