Delete app.py

app.py

@@ -1,83 +0,0 @@
-import gradio as gr 
-
-
-import torch
-
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-#class names 
-with open('class_names.txt', "r") as f:
-    class_names  = [car.strip() for car in  f.readlines()]
-
-
-#model and transforms preparation
-effnetb0_weights = models.EfficientNet_B0_Weights.DEFAULT
-effnetb0 = torchvision.models.efficientnet_b0(weights = effnetb0_weights)
-effnetb0_transforms = effnetb0_weights.transforms()
-
-#freeze params 
-for param in effnetb0.parameters(): 
-  param.requires_grad = False 
-        
-#change classifier 
-effnetb0.classifier = nn.Sequential(
-    nn.Dropout(p=.2),
-    nn.Linear(in_features = 1280, 
-              out_features = 196)
-)
-
-#load saved weights 
-effnetb0.load_state_dict(torch.load('stanford_cars/pretrained_effnetb0_stanford_cars_20_percent.pth.pth'), 
-                          map_location=torch.device("cpu"))
-
-
-#predict function 
-
-def predict(img) -> Tuple[Dict, float]:
-
-  start_time = timer()
-
-  #put model into eval mode 
-  effnetb0.eval()
-
-  with torch.inference_mode():
-    pred_logits = effnetb0(img.unsqueeze(0))
-    pred_probs = torch.softmax(pred_logits, dim = 1)
-
-   # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
-  pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
-
-  end_time = timer()
-
-  time = round(end_time - start_time, 5) 
-
-  return pred_labels_and_probs, time
-
-
-#gradio app 
-
-title = 'effnetb0'
-description = 'Pretrained effnetb0 model on stanford cars dataset'
-
-#create example list 
-example_list = [["examples/" + example] for example in os.listdir("examples")]
-
-# Create Gradio interface 
-demo = gr.Interface(
-    fn=predict,
-    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
-    
-)
-
-# Launch the app!
-demo.launch()
-
-