Upload app.py

app.py

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+### imports and class names setup ###
+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
+
+# setting up class names    
+with open("class_names.txt", "r") as f:
+  class_names = [food.strip() for food in f.readlines()]
+
+### model and transforms prepration ###
+effnetb2, effnetb2_transforms = create_effnetb2_model(num_classes=101,
+                                                      seed=29)
+# loading the saved weights
+effnetb2.load_state_dict(
+  torch.load(
+    f="pretrained_effnetb2_feature_extractor_food101_20_percent.pth",
+    map_location=torch.device("cpu") # loading the model to cpu
+  )
+)
+
+### predict function ###
+def predict(img) -> Tuple[Dict, float]:
+  # start a timer
+  start_time = timer()
+
+  # transforming the input image
+  img = effnetb2_transforms(img).unsqueeze(0)
+
+  # putting the model into eval mode & making prediction
+  effnetb2.eval()
+  with torch.inference_mode():
+    # passing transformed img through the model and turn pred logits into probs
+    pred_probs = torch.softmax(effnetb2(img), dim=1)
+
+  # creating a prediction label & prediction probability dictionary
+  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
+
+### gradio app ###
+# creating title, description and article
+title = "FoodVision Big"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images in 101 different classes!"
+
+# creating an example list
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# creating the gradio demo
+demo = gr.Interface(fn=predict, # maps inputs to outputs
+                    inputs=gr.Image(type="pil"),
+                    outputs=[gr.Label(num_top_classes=5, label="Predictions"),
+                             gr.Number(label="Prediction Time (s)")],
+                    examples=example_list,
+                    title=title,
+                    description=description)
+
+# launching the demo
+demo.launch()