Delete imageApp(ImageClassificationModel).py

imageApp(ImageClassificationModel).py

@@ -1,64 +0,0 @@
-
-## 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