Update app.py

app.py

@@ -1,9 +1,57 @@
 import streamlit as st
+from io import StringIO
+import requests
 
+import torch
+from torchvision.models.inception import inception_v3
+import matplotlib.pyplot as plt
+from skimage.transform import resize
 
-st.markdown("### Hello dude!")
+@st.cache
+def load_stuff():
+    model = inception_v3(pretrained=True,      # load existing weights
+	             transform_input=True, # preprocess input image the same way as in training
+	            )
+
+    model.aux_logits = False # don't predict intermediate logits (yellow layers at the bottom)
+    model.train(False)
+
+    LABELS_URL = 'https://raw.githubusercontent.com/anishathalye/imagenet-simple-labels/master/imagenet-simple-labels.json'
+    labels = {i: c for i, c in enumerate(requests.get(LABELS_URL).json())}
+    return model, labels
+    
+
+model, labels = load_stuff()
+
+
+def transform_input(img):
+    return torch.as_tensor(img.reshape([1, 299, 299, 3]).transpose([0, 3, 1, 2]), dtype=torch.float32)
 
 
-text = st.text_area("please enter text", value="dummy")
-output = text[::-1]
-st.markdown(output)
+def predict(img):
+    img = transform_input(img)
+    probs = torch.nn.functional.softmax(model(img), dim=-1)
+    probs = probs.data.numpy()
+    top_ix = probs.ravel().argsort()[-1:-10:-1]
+    s = 'top-10 classes are: \n\n [prob : class label]\n\n'
+    for l in top_ix:
+        s = s + '%.4f :\t%s' % (probs.ravel()[l], labels[l].split(',')[0]) + '\n\n'
+    return s
+
+
+
+st.markdown("### Hello dude!")
+
+uploaded_file = st.file_uploader("Choose a file")
+if uploaded_file is not None:
+    # To read file as bytes:
+    bytes_data = uploaded_file.getvalue()
+    
+    
+    with open('tmp', 'wb')as f:
+       f.write(bytes_data)
+    img = resize(plt.imread('tmp'), (299, 299))[..., :3]
+    
+    top_classes = predict(img)
+    st.markdown(top_classes)
+    st.image('tmp')