first commit

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth filter=lfs diff=lfs merge=lfs -text
+examples/*.jpg filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a0f7b7d5632c47bb99cb053f638038073b83d02f8ba460641caddb0096437be
+size 31314554

app.py

@@ -0,0 +1,63 @@
+# Step 1
+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
+
+## Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+# Step 2
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=len(class_names))
+effnetb2.load_state_dict(torch.load(f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
+                                   map_location=torch.device("cpu"), weights_only = True))
+
+# Step 3
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Timer start
+    start_time = timer()
+
+    # Transform the image and add a batch dimension
+    img = effnetb2_transforms(img).unsqueeze(0)
+
+    # Get model into eval() mode and turn on inference mode
+    effnetb2.eval()
+    with torch.inference_mode():
+        # Pass transformed image through the model and turn pred logits to pred probs
+        pred_logits = effnetb2(img)
+        pred_probs = torch.softmax(pred_logits, dim = 1)
+
+    # Create pred label and pred prob dict for each pred class (this is the reqd format for Gradio's output parameter)
+    pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
+
+    # Calculate the pred time
+    pred_time = round(timer() - start_time, 5)
+
+    # return pred dict and pred time
+    return pred_labels_and_probs, pred_time   
+
+# Step 4
+## Create title, description and article strings
+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/)."
+
+## Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+## Create the Gradio demo
+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)
+
+## Launch the demo
+demo.launch()

model.py

@@ -0,0 +1,25 @@
+import torch
+import torchvision
+from torch import nn
+
+# Functionalize the EffNetB2 feature extractor model creation
+def create_effnetb2_model(num_classes: int=3, seed: int=42):
+    """Creates an EfficientNetB2 feature extractor model and its transforms.
+    Returns the model and transforms.
+    """
+    # 1, 2, 3 Steps here
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=weights)
+
+    # Step 4
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Step 5
+    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

@@ -0,0 +1,3 @@
+torch==2.5.0
+torchvision==0.20.0
+gradio==5.44.1