Upload app.py

app.py

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+
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple,Dict
+
+with open('class_names.txt','r') as f:
+  class_names = [food.strip() for food in f.readlines()]
+
+effnetb2 , effnetb2_transforms = create_effnetb2_model(num_classes=101)
+
+effnetb2.load_state_dict(torch.load(f='09_pretrained_effnetb2_feature_extractor_food101_20_percent.pth',
+                                    map_location= torch.device('cpu')))
+
+def predict(img) -> Tuple[Dict,float]:
+
+  start_time = timer()
+
+  img = effnetb2_transforms(img).unsqueeze(0)
+
+  effnetb2.eval()
+  with torch.inference_mode():
+    pred_prob  = torch.softmax(effnetb2(img),dim=1)
+  pred_labels_and_probs = {class_names[i]:float(pred_prob[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
+
+
+
+
+# Create title, description and article strings
+title = "FoodVision Big 🍔👁"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food into [101 different classes](https://github.com/mrdbourke/pytorch-deep-learning/blob/main/extras/food101_class_names.txt)."
+example_list = [['examples'+example] for example in os.listdir('examples') ]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict, # mapping function from input to output
+                    inputs=gr.Image(type="pil"), # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=3, label="Predictions"), # what are the outputs?
+                             gr.Number(label="Prediction time (s)")], # our fn has two outputs, therefore we have two outputs
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    )
+
+# Launch the demo!
+demo.launch() # generate a publically shareable URL?