Create app.py

app.py

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+# pretrained Resnet-18 mode
+import torch
+model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
+
+# define a function that takes in the user input, which in this case is an image, and returns the prediction. 
+'''The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities. 
+We will load the class names from this text file.
+'''
+import requests
+from PIL import Image
+from torchvision import transforms
+
+# Download human-readable labels for ImageNet.
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+def predict(inp):
+ inp = transforms.ToTensor()(inp).unsqueeze(0)
+ with torch.no_grad():
+  prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
+  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
+ return confidences
+
+ '''The function converts the input image into a PIL Image and subsequently into a PyTorch tensor. 
+ After processing the tensor through the model, it returns the predictions in the form of a dictionary named confidences. 
+ The dictionary's keys are the class labels, and its values are the corresponding confidence probabilities.
+
+In this section, we define a predict function that processes an input image to return prediction probabilities. 
+The function first converts the image into a PyTorch tensor and then forwards it through the pretrained model. 
+
+We use the softmax function in the final step to calculate the probabilities of each class. 
+The softmax function is crucial because it converts the raw output logits from the model, which can be any real number, into probabilities that sum up to 1. 
+This makes it easier to interpret the model’s outputs as confidence levels for each class.'''
+
+# Creating a Gradio interface
+import gradio as gr
+
+gr.Interface(fn=predict,
+       inputs=gr.Image(type="pil"),             # creates the component and handles the preprocessing to convert that to a PIL image
+       outputs=gr.Label(num_top_classes=3),     # a Label, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images by constructing it as 
+       examples=["/lion.jpg", "/cheetah.jpg"]).launch()