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+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text
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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:eff07ee6a9faf1b1cbaf25837bd5990025f46ac083ea629919de57c82a86c157
+size 31314554

README.md

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----
-title: Food Vision Mini
-emoji: 📊
-colorFrom: yellow
-colorTo: green
-sdk: gradio
-sdk_version: 5.8.0
-app_file: app.py
-pinned: false
-license: mit
-short_description: Food Vision Mini Project
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: Food Vision Mini
+emoji: 📊
+colorFrom: yellow
+colorTo: green
+sdk: gradio
+sdk_version: 5.8.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: Food Vision Mini Project
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+### 1. Imports and class names setup
 import gradio as gr
+import os
+import torch
 
-def greet(name):
-    return "Hello " + name + "!"
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Setup class names
+class_names = ['pizza', 'steak', 'sushi']
+
+### 2. Model and transforms preparation
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=3)
+
+# Load saved weights
+effnetb2.load_state_dict(
+    torch.load(
+        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+        map_location=torch.device("cpu") # load the model to the CPU
+    )
+)  
+### Predict function
+
+def predict(img) -> Tuple[Dict, float]:
+  # Start a timer
+  start_time = timer()
+
+  # Transform the input image for use with EffNetB2
+  img = effnetb2_transforms(img).unsqueeze(0)  # unsqueeze = add batch dimension on 0th index.
+
+  # put model into eval mode, make predictions
+  effnetb2.eval()
+  with torch.inference_mode():
+    # Pass transformed image through model and turn the prediction logits into probabilities
+    pred_probs = torch.softmax(effnetb2(img), dim=1)
+
+    # Create a prediction label and prediction probability dictionary
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    # Calculate pred time
+    end_time = timer()
+    pred_time = round(end_time - start_time, 4)
+
+    # Return pred dict and pred time
+    return pred_labels_and_probs, pred_time
+
+## 4. Gradio app
+
+# Create title, description and article. 
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An [EfficientNetB2 feature extractor](https://pytorch.org/vision/stable/models/generated/torchvision.models.efficientnet_b2.html#torchvision.models.efficientnet_b2) computer vision model to classify images as pizza, steak or sushi."
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/#74-building-a-gradio-interface)."
+
+# Create example list
+example_list = [["examples/" + example] for exmple in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict,  # maps inputs to outputs
+                    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)
+                    
+# Launch the demo!
+demo.launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=3,  # default output class = 3
+                          seed:int=42):
+  # 1, 2, 3 Create EffnetB2 pretrained weights, transforms and model
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transforms = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights=weights)
+
+  # 4. Freeze all layers of the base model
+  for param in model.parameters():
+    param.requires_grad = False
+
+  # 5. Change classifier head with random seed for reproducability
+  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=1.12.0
+torchvision==0.13.0
+gradio==3.1.4