Upload app.py.py

app.py.py

@@ -0,0 +1,64 @@
+
+## create streamlit app
+
+# import required libraries and modules
+import json
+import numpy as np
+import matplotlib.pyplot as plt
+
+import torch
+from PIL import Image
+from torchvision import transforms
+from torchvision.models import densenet121
+
+import streamlit as st
+
+# define prediction function
+def predict(image):
+    # load DL model
+    model = densenet121(pretrained=True)
+
+    model.eval()
+
+    # load classes
+    with open('imagenet_class_index.json', 'r') as f:
+        classes = json.load(f)
+
+    # preprocess image
+    preprocess = transforms.Compose([
+        transforms.Resize(256),
+        transforms.CenterCrop(224),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+    input_tensor = preprocess(input_image)
+    input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model
+
+    # get prediction
+    with torch.no_grad():
+        output = model(input_batch)
+
+    pred = torch.nn.functional.softmax(output[0], dim=0).cpu().numpy()
+
+    # return confidence and label
+    confidence = round(max(pred)*100, 2)
+    label = classes[str(np.argmax(pred))][1]
+
+    return confidence, label
+
+# define image file uploader
+image = st.file_uploader("Upload image here")
+
+# define button for getting prediction
+if image is not None and st.button("Get prediction"):
+    # load image using PIL
+    input_image = Image.open(image)
+
+    # show image
+    st.image(input_image, use_column_width=True)
+
+    # get prediction
+    confidence, label = predict(input_image)
+
+    # print results
+    "Model is", confidence, "% confident that this image is of a", label