Added my Gradio app files

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f38043ceac7cfc1e790ed9dbccfff42c320b7d7b6f7bbfb9ce1249cd329613af
+size 31314554

app.py

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+
+import gradio as gr
+import os
+import torch
+from model import create_effnetb2_model
+from timeit import default_timer as Timer
+from typing import Tuple,Dict
+class_names=["pizza","steak","sushi"]
+effnetb2,effnetb2_transforms=create_effnetb2_model(num_classes=3)
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu")
+    )
+)g
+
+def predict(img) -> Tuple[Dict,float]:
+    start_time = timer()
+    img = effnetb2_transforms(img).unsqueeze(0)
+    
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs = torch.softmax(effnetb2(img), dim=1)
+    
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+    pred_time = round(timer() - start_time, 5)
+    return pred_labels_and_probs, pred_time
+
+
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+demo = gr.Interface(fn=predict, 
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), 
+                             gr.Number(label="Prediction time (s)")], 
+                    examples=example_list, 
+                    title=title,
+                    description=description,
+                    article=article)
+
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes:int=3, 
+                          seed:int=42):
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    for param in model.parameters():
+        param.requires_grad = False
+
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes),
+    )
+    
+    return model, transforms

requirements.txt

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+torch==latest
+torchvision==latest
+gradio==3.45.0