Create app.py

app.py

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+import torch
+import torchvision
+from torchvision import transforms
+import numpy as np
+import gradio as gr
+from PIL import Image
+from pytorch_grad_cam import GradCAM
+from pytorch_grad_cam.utils.image import show_cam_on_image
+from resnet_lightning import ResNet18Model
+import gradio as gr
+
+model = ResNet18Model.load_from_checkpoint("epoch=19-step=3920.ckpt")
+
+inv_normalize = transforms.Normalize(
+    mean = [-0.50/0.23, -0.50/0.23, -0.50/0.23],
+    std= [1/0.23, 1/0.23,1/0.23]
+)
+
+classes = ('plane', 'car', 'bird', 'cat', 'deer',
+           'dog', 'frog', 'horse', 'ship', 'truck')
+
+def resize_image_pil(image, new_width, new_height):
+    img =  Image.fromarray(np.array(image))
+    width, height = img.size
+
+    width_scale = new_width/width
+    height_scale - new_height/height
+    scale = min(width_scale, height_scale)
+    resized = img.resize((int(width*Scale), int(height*Scale)), Image.NEAREST)
+    resized = resized.crop((0,0,new_width, new_height))
+
+    return resized
+
+
+def inference(input_img, transparancy = 0.5, target_layer_number = -1):
+    input_img = resize_image_pil(input_img,32,32)
+    input_img = np.array(input_img)
+    org_img = input_img
+    input_img= input_img.reshape((32,32,3))
+    transform = transforms.ToTensor()
+    input_img = transform(input_img)
+    input_img = input_img.unsqueeze(0)
+    outputs = model(input_img)
+    softmax =  torch.nn.Softmax(dim=0)
+    o = softmax(outputs.flattern())
+    confidences = {classes[i]:float(o[i] fir i in range(10))}
+    -, prediction= torch.max(outputs,1)
+    target_layers = [model.layer2[target_layer_number]]
+    cam = GradCAM(model=model, target_layers=target_layers)
+    grayscale_cam = cam(input_tensor= input_img,target=None)
+    grayscale_cam = grayscale_cam[0, :]
+    visualization = show_cam_on_image(org_img/255,grayscale_cam,use_rgb=True,
+                                     image_weight=transparancy)
+    
+    return classes[prediction[0].item(),visualization,confidences]
+    
+demo = gr.Inference(
+    inference,
+    inputs = [
+        gr.Image(width=256,height=256,label="input image"),
+        gr.Slider(0,1,value=0.5,label="Overall opacity of the overelay"),
+        gr.Slider(-2,-1, value =-2, step=1, label= "Which layer for Gradcam")
+    ],
+    outputd = [
+        "text",
+        gr.IMage(width= 256, height=256,label="Output"),
+        gr.Label(num_top_classes=3)        
+    ],
+    title = "CIFAR 10 trained on ResNet model in pytorch lightning with Gradcam"
+    description = " A simple gradio inference to infer on resnet18 model"
+    examples = [["cat.jpg", 0.5, -1],["dog.jpg",0.7,-2]]
+)
+
+demo.launch()