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_20_percent.pth filter=lfs diff=lfs merge=lfs -text
+*pth filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_20_percent.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66cbafff5c12080c4c626f0b6099b8434b092f67990af02a3db861a2e2056406
+size 31299697

app.py

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+
+import torch
+import torchvision
+from torch import nn
+from timeit import default_timer as timer
+from typing import Tuple,Dict
+import os
+from model import create_effnetb2
+
+# gettingthe classnames
+class_names = ["pizza","steak","sushi"]
+
+#lets verify if we can get list of example
+foodvision_mini_examples_path = "examples/"
+example_list = ["examples/" + example for example in os.listdir(foodvision_mini_examples_path)]
+
+# getting model and its trasofrm
+effnetb2_2, effnetb2_transforms_2 = model.create_effnetb2()
+
+# load the saved weigths
+effnetb2_2.load_state_dict(
+    torch.load(
+        f = "09_pretrained_effnetb2_feature_extractor_20_percent",
+        map_location= torch.device("cpu")
+    )
+)
+
+# craeting the predict function
+def predict(img) -> Tuple[Dict,float]:
+  # start a timer
+  start_time = timer()
+
+  # transfomr the input image for use with EffNetB2
+  transformed_img = effnetb2_transforms_2(img).unsqueeze(0).to("cpu")
+
+  # put the mdoel to eva; mode and make predictions
+  effnetb2.eval()
+  with torch.inference_mode():
+    logits = effnetb2_2(transformed_img)
+    pred_probs = torch.softmax(logits, dim = 1)
+    # print(pred_probs)
+    pred_class = class_names[torch.argmax(pred_probs, dim = 1)]
+
+    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
+
+# interface zone
+import gradio as gr
+
+# craete title , description and article
+title = "Food Vision Mini 🍕🥩🍣"
+description = "An [EfficientNetB2 Feature Extractor Computer Vision Model](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html) to classify images as pizza, steak and sushi"
+article = "Created at [09 PyTorch model deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)"
+
+# craeting gradio demo
+demo = gr.Interface(fn = predict,inputs = gr.Image(type = "pil"),
+                    outputs = [gr.Label(num_top_classes=3, label = "predictions") , gr.Number(label = "prediciton time(s)")],
+                    examples = example_list,
+                    title = title , description = description, article = article)
+demo.launch(debug = False,share = True) # to avoid showing of error

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2(seed : int = 42, num_classes : int = 3):
+  #1,2,3 create model , weights and transforms
+  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+  transform = weights.transforms()
+  model = torchvision.models.efficientnet_b2(weights = weights)
+
+  # frezzing the base layers
+  for param in model.parameters():
+    param.requires_grad = False
+
+  #5 updating the clasiifier head for our model
+  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, transform

requirements.txt

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+torch == 2.0.1
+torchvision == 0.15.2
+gradio == 3.39.0