Create app.py

app.py

@@ -0,0 +1,34 @@
+st.markdown('<h1 style="color:black;">Vgg 19 Image classification model</h1>', unsafe_allow_html=True)
+st.markdown('<h2 style="color:gray;">The image classification model classifies image into following categories:</h2>', unsafe_allow_html=True)
+st.markdown('<h3 style="color:gray;"> street,  buildings, forest, sea, mountain, glacier</h3>', unsafe_allow_html=True)
+
+@st.cache(allow_output_mutation=True)
+def get_base64_of_bin_file(bin_file):
+    with open(bin_file, 'rb') as f:
+        data = f.read()
+    return base64.b64encode(data).decode()
+
+
+upload= st.file_uploader('Insert image for classification', type=['png','jpg'])
+c1, c2= st.columns(2)
+if upload is not None:
+  im= Image.open(upload)
+  img= np.asarray(im)
+  image= cv2.resize(img,(224, 224))
+  img= preprocess_input(image)
+  img= np.expand_dims(img, 0)
+  c1.header('Input Image')
+  c1.image(im)
+  c1.write(img.shape)
+  input_shape = (224, 224, 3)
+  optim_1 = Adam(learning_rate=0.0001)
+  n_classes=6
+  vgg_model = model(input_shape, n_classes, optim_1, fine_tune=2)
+  vgg_model.load_weights('/content/drive/MyDrive/vgg/tune_model19.weights.best.hdf5')
+
+  # prediction on model
+  vgg_preds = vgg_model.predict(img)
+  vgg_pred_classes = np.argmax(vgg_preds, axis=1)
+  c2.header('Output')
+  c2.subheader('Predicted class :')
+  c2.write(classes[vgg_pred_classes[0]] )