Add application file

.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
+*.psd filter=lfs diff=lfs merge=lfs -text

09_pretrained_effnetb2_feature_extractor_pizza20%_10epochs.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a1315edc7d6b4e0c8f633752d463e0022abf1bb77ec8a2edaa365dff11018549
+size 31297210

app.py

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+
+from model import create_effnetb2
+from typing import Tuple, Dict
+from PIL import Image
+
+from time import time
+import torch
+import torchvision
+import gradio as gr
+import os
+
+from pathlib import Path
+
+class_names = ["pizza", "steak", "sushi"]
+
+effnetb2 , effnetb2_transforms = create_effnetb2()
+# Load weights
+PATH = "09_pretrained_effnetb2_feature_extractor_pizza20%_10epochs.pth"
+
+effnetb2.load_state_dict(torch.load(f=PATH,
+                                   map_location=torch.device('cpu')
+                                   ))
+effnetb2.eval()
+
+
+
+
+def predict(img) ->Tuple[Dict, float]:
+    start_time = time()
+
+    img_tr = img
+    img_tr = effnetb2_transforms(img_tr).unsqueeze(0)
+    #predict
+    effnetb2.eval()
+    with torch.inference_mode():
+     
+        pred_prob = torch.softmax(effnetb2(img_tr), dim=1)
+       
+
+    pred_labesls_and_pobs ={class_names[i]:pred_prob[0][i] for i in range(len(class_names)) }
+    
+ 
+
+
+    end_time = time()
+    pred_time =  round(end_time - start_time,4)
+    return pred_labesls_and_pobs ,pred_time
+
+
+example_list = [["examples/"+example for example in os.listdir("examples") ]]
+
+# Create title, description and article strings
+title = "FoodVision Classification"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+article = "Created at [Using pre-trained model efficientnet_b2](https://pytorch.org/vision/main/models/generated/torchvision.models.efficientnet_b2.html)."
+
+# 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(debug=False,
+            share=True)#72H
+
+

model.py

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+
+import torch
+import torchvision
+from torch import nn
+
+
+
+def create_effnetb2():
+    torch.manual_seed(42)
+    torch.cuda.manual_seed(42)
+
+
+    effnetb2_weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = effnetb2_weights.transforms()
+    model = torchvision.models.efficientnet_b2(weights=effnetb2_weights)
+    for param in model.parameters():
+        param.requires_grad = False
+
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True), 
+        nn.Linear(in_features=1408, 
+                  out_features=3)) 
+    model.name ='effnetb2'
+    return model, transforms
+
+

requirements.txt

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+torch==2.1.2
+torchvision==0.16.2
+gradio==4.12.0