Update app.py

app.py

@@ -1,3 +1,49 @@
-import os
+import gradio as gr
 
-os.system("pip3 install --upgrade gradio")
+import torch
+import torch.nn.functional as F
+from torchvision import transforms
+
+# load model
+model = torch.jit.load("food_classifier.ptl")
+
+# Transformations that will be applied
+the_transform = transforms.Compose([
+        transforms.Resize((224,224)),
+        transforms.CenterCrop((224,224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])
+])
+
+# Classes 
+class_names = ['Apple Pie','Bibimbap','Cannoli','Edamame','Falafel','French Toast','Ice Cream','Ramen','Sushi','Tiramisu']
+
+# Returns transformed image
+def transform_img(img):
+    return the_transform(img) 
+    
+# Returns string with class and probability
+def classify_img(img):
+    # Applying transformation to the image
+    model_img = transform_img(img)
+    model_img = model_img.view(1,3,224,224)
+    
+    # Running image through the model
+    model.eval()
+    with torch.no_grad():
+        result = model(model_img)
+    
+    # Converting values to softmax values 
+    result = F.softmax(result,dim=1)
+    probability = round(result[0][result.argmax()].item() * 100, 2)
+    
+    # Returning class name and probability
+    return f'{class_names[result.argmax()]} : {probability}% confident'
+    
+demo = gr.Interface(classify_img,
+                        inputs = gr.inputs.Image(type="pil"), 
+                        outputs = "text",
+                        title = "Food Classifier!", 
+                        description="Insert food you would like to classify! <br> Categories: Apple Pie, Bibimbap, Cannoli, Edamame, Falafel, French Toast, Ice Cream, Ramen, Sushi, Tiramisu")
+
+demo.launch(inline=False)