Add application files

.gitattributes

@@ -33,3 +33,4 @@ 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
+effnetB2_2025-11-29_epoch4.pt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,81 @@
+import torch
+from torchvision.models import EfficientNet_B2_Weights
+import gradio as gr
+from model import EffnetB2
+from timeit import default_timer as timer
+from typing import Tuple
+import os
+
+
+device = "cpu"
+
+# laoding our model
+model = EffnetB2()
+checkpoint = torch.load(
+    "effnetB2_2025-11-29_epoch4.pt",
+    map_location=device,
+)
+model.load_state_dict(checkpoint)
+model.to(device)
+
+class_names = ["pizza", "steak", "sushi"]
+
+
+def predict(img) -> Tuple[dict, float]:
+    """_summary_
+    Takes an image and make predictions
+    Args:
+        img (_type_): An Image
+
+    Returns:
+        Tuple[dict, float]: a dict for the confidence of each class, and float for the inference time
+    """
+
+    # Transform the image to work with effnetB2
+    transform = (
+        EfficientNet_B2_Weights.DEFAULT.transforms()
+    )  # getting the model transforms
+    transformed_img = transform(img).unsqueeze(0).to(device)
+
+    # Put model into eval mode and make predictions
+    start = timer()  # start timer
+    model.eval()
+
+    with torch.inference_mode():
+        logits = model(transformed_img)
+        pred_probs = torch.softmax(logits, dim=1)
+        pred_labels = torch.argmax(pred_probs, dim=1)
+
+    # Creating a prediction label and a preiction probability dict
+    pred_dict = {
+        k: v for k, v in zip(class_names, pred_probs.squeeze(0).cpu().tolist())
+    }
+    end = timer()
+    pred_time = round(end - start, 4)
+    return (pred_dict, pred_time)
+
+
+# Creating a list of exmaple images for Gradio Demo
+example_list = [
+    ["examples/" + example]
+    for example in os.listdir("examples")
+]
+
+title = "Food Vision Mini 🍕🥩🍣"
+description = "An efficientNetB2 feature extractor computr vision model to classify images as pizza, steak and sushi."
+article = "Created at [09.Pytorch Model Deployement.](https://www.learnpytorch.io/09_pytorch_model_deployment/)"
+
+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(debug=False, share=True)

effnetB2_2025-11-29_epoch4.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:252bc61e6a438b7c8a59c5141cc63d09bad1972a3f413be35b6f55774f841a3e
+size 31289914

model.py

@@ -0,0 +1,20 @@
+import torch.nn as nn
+from torchvision.models import efficientnet_b2, EfficientNet_B2_Weights
+
+
+class EffnetB2(nn.Module):
+    def __init__(self, num_classes=3):
+        super().__init__()
+        self.model = efficientnet_b2(weights=EfficientNet_B2_Weights.DEFAULT)
+        for param in self.model.parameters():
+            param.requires_grad = False
+        # print(self.model)
+        in_features = self.model.classifier.get_submodule("1").in_features
+        self.model.classifier = nn.Sequential(
+            nn.Linear(in_features=in_features, out_features=num_classes)
+        )
+
+    def forward(self, x):
+        return self.model(x)
+
+

requirements.txt

@@ -0,0 +1,4 @@
+torch
+torchvision
+gradio
+timeit