Delete app.py

app.py

@@ -1,45 +0,0 @@
-
-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
-class_names=["pizza","steak","sushi"]
-effnetb2,effnetb2_transforms=create_effnetb2_model(num_classes=3)
-effnetb2.load_state_dict(
-    torch.load(
-        f="09_pretrained_effnetb2_feature_extractor_pizza_steak_sushi_20_percent.pth",
-        map_location=torch.device("cpu")
-    )
-)g
-
-def predict(img) -> Tuple[Dict,float]:
-    start_time = timer()
-    img = effnetb2_transforms(img).unsqueeze(0)
-    
-    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))}
-    pred_time = round(timer() - start_time, 5)
-    return pred_labels_and_probs, pred_time
-
-
-title = "FoodVision Mini 🍕🥩🍣"
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
-article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
-
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-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)
-
-demo.launch()