first commit. Added all files

.gitattributes

@@ -32,3 +32,5 @@ 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_effnet_b2_feature_extractor_20%.pth filter=lfs diff=lfs merge=lfs -text
+.pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnet_b2_feature_extractor_20%.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bea198b7a2ad0730c7370bdf84475ac3183ba05a662849b4a3c13ae5c79a3a50
+size 31296189

app.py

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+import gradio as gr
+import os
+import torch
+from model import create_effnet_b2_model
+from timeit import default_timer as timer
+from typing import List, Dict
+
+class_names = ['pizza', 'steak', 'sushi']
+
+effnetb2, effnetb2_transforms = create_effnet_b2_model(
+    num_classes=3)
+
+#load weigths
+effnetb2.load_state_dict(
+    torch.load(
+        f='09_pretrained_effnet_b2_feature_extractor_20%.pth',
+        map_location=torch.device('cpu')
+    )
+)
+
+
+#predict
+def predict(img) -> Tuple[Dict, float]:
+    #start a timer
+    start_time = timer()
+    #transform input image
+    img = effnetb2_transforms(img).unsqueeze(0)
+    #set model to eval mode
+    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))}
+    end_time = timer()
+    pred_time = round(end_time - start_time, 4)
+
+    return pred_labels_and_probs, pred_time
+
+
+examples_list = [['examples/' + example] for example in os.listdir('examples')]
+examples_list
+
+
+
+title = 'foodvision mini'
+description = 'effnet feature extractor for image classification'
+article = 'course type-along'
+
+
+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)

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+def create_effnet_b2_model(num_classes : int = 3,
+                           seed : int = 42):
+    
+    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    effnetb2_transforms = effnetb2_weights.transforms()
+
+    effnetb2  = torchvision.models.efficientnet_b2(weights=effnetb2_weights)
+
+    for p in effnetb2.parameters():
+        p.requires_grad = False 
+
+    torch.manual_seed(seed)
+    #torch.cuda.manual_seed(seed)
+    effnetb2.classifier = nn.Sequential(
+                 torch.nn.Dropout(p=0.3,
+                                  inplace=True),
+                torch.nn.Linear(in_features=1408,
+                                out_features=num_classes,
+                                bias=True)
+)
+
+    return effnetb2, effnetb2_transforms

requirements.txt

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+torch==2.0.1
+torchvision==0.15.2
+gradio==3.32.0