Create app.py

app.py

@@ -0,0 +1,73 @@
+"""
+Ryan Tietjen
+Aug 2024
+Demo application for a food classificiation demonstration
+"""
+from model import vit_b_16
+from timeit import default_timer as timer
+import torch
+import os
+import gradio as gr 
+
+with open("demo/class_names.txt", 'r') as file:
+    class_names = [line.strip() for line in file]
+
+
+model, transforms = vit_b_16(num_classes=101,
+                             seed=31,
+                             freeze_gradients=True,
+                             unfreeze_blocks=0)
+
+model.load_state_dict(torch.load('demo/vit_b_16_unfreeze_one_encoder_block_10_total_epochs.pth',
+                                  weights_only=True))
+
+
+def predict_single_image(img):
+    start_time = timer()
+
+    model.eval()
+
+    #Add batch dim
+    img = transforms(img).unsqueeze(dim=0)
+
+    with torch.inference_mode():
+        # Obtain prediction logits -> prediction probabilities from image
+        logits = model(img)
+        probabilities = torch.softmax(logits, dim=1)
+
+        class_probabilities = {}
+        for i in range(len(class_names)):
+            class_probabilities[class_names[i]] = float(probabilities[0][i])
+
+    end_time = timer()
+
+    pred_time = round(end_time - start_time, 3)
+
+    return class_probabilities, pred_time
+
+
+title = "Food Image Classification With PyTorch by Ryan Tietjen"
+description = f"""
+Determines what type of food is presented in a given image. 
+This model is capable of classifying [101 different types of food](https://github.com/RyanTietjen/Food-Classifier-pytorch-ver.-/blob/main/demo/class_names.txt) by 
+utilizing a [pre-trained Vision Transformer](https://pytorch.org/vision/stable/models/generated/torchvision.models.vit_b_16.html#torchvision.models.ViT_B_16_Weights),
+and fine-tuning the results for specific food categories.
+This model achieved a Top-1 accuracy of 91.55% and a Top-5 accuracy of 98.56%
+"""
+
+sample_list = [["demo/samples/" + sample] for sample in os.listdir("demo/samples")]
+
+#Gradio interface
+demo = gr.Interface(
+    fn=predict_single_image,
+    inputs=gr.Image(type="pil"),
+    outputs=[
+        gr.Label(num_top_classes=5, label="Predictions"),
+        gr.Number(label="Prediction time (s)"),
+    ],
+    examples=sample_list,
+    title=title,
+    description=description,
+)
+
+demo.launch(share=True)