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README.md

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 ---
+title: Food Vision Mini
+emoji: 💻
+colorFrom: green
+colorTo: green
+sdk: gradio
+sdk_version: 5.26.0
+app_file: app.py
+pinned: false
 license: mit
+short_description: An EfficientNetB2 feature extractor computer vision model to
 ---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+import os
+import torch
+from model import create_model
+from timeit import default_timer as timer
+class_names=["Pizza","Steak","Sushi"]
+effnetb2,effnetb2_transform=create_model(num_class=3)
+effnetb2.load_state_dict(torch.load(f="effnetb2_pizza_steak_sushi (1).pt",map_location=torch.device('cpu')))
+
+
+def predict(img):
+  start_time=timer()
+  img=effnetb2_transform(img).unsqueeze(0)
+  effnetb2.eval()
+  with torch.inference_mode():
+    predict_logit=effnetb2(img)
+    predict_prob=torch.softmax(predict_logit,dim=1)
+    predict_label=torch.argmax(predict_prob,dim=1)
+  pred_label_prob={class_names[i]:float(predict_prob[0][i]) for i in range(len(class_names))}
+  end_time=timer()
+  pred_time=round(end_time-start_time,4)
+  return pred_label_prob,pred_time
+
+title="Food Vision Mini 🍕🥩🍣"
+description="An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+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="Prediction"),gr.Number(label="Prediction time")],examples=example_list,title=title,description=description)
+demo.launch()

effnetb2_pizza_steak_sushi (1).pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:ee17107d6aa14ae47b961ab7b51e0a0a9c9ba04b5e35bf2fa546cca1d15677b5
+size 31284026

model.py

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+import torch
+import torchvision
+from torchvision import transforms
+from torch import nn
+def create_model(num_class:int=3,seed:int=42):
+    weights=torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transform=weights.transforms()
+    model=torchvision.models.efficientnet_b2(weights=weights)
+    for params in model.parameters():
+        params.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_class, bias=True))
+    return model,transform

requirements.txt

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+gradio==5.26.0
+torch==2.6.0
+torchinfo==1.8.0
+torchmetrics==1.7.0
+torchvision==0.21.0
+tqdm==4.66.5
+transformers==4.50.3