Delete app.py

app.py

@@ -1,41 +0,0 @@
-import os
-import torch
-import torchvision.transforms as transforms
-import torch.nn.functional as F
-import gradio as gr
-from urllib.request import urlretrieve
-from model import Net
-
-# Loads latest model state from Github
-urlretrieve("https://github.com/equ1/mnist-interface/tree/main/saved_models")
-
-model_timestamps = [filename[10:-3]
-                    for filename in os.listdir("./saved_models")]
-latest_timestamp = max(model_timestamps)
-
-if torch.cuda.is_available():
-  dev = "cuda:0"
-else:
-  dev = "cpu"
-
-device = torch.device(dev)
-
-model = Net()
-model.load_state_dict(torch.load(f"./saved_models/mnist-cnn-{latest_timestamp}.pt", map_location=device))
-model.eval()
-
-# inference function
-def inference(img):
-    transform = transforms.Compose([transforms.ToTensor(), transforms.Resize((28, 28))])
-    img = transform(img).unsqueeze(0)  # transforms ndarray and adds batch dimension
-
-    with torch.no_grad():
-        output_probabilities = F.softmax(model(img), dim=1)[0]  # probability prediction for each label
-
-    return {labels[i]: float(output_probabilities[i]) for i in range(len(labels))}
-
-# Creates and launches gradio interface
-labels = range(10)  # 1-9 labels
-outputs = gr.outputs.Label(num_top_classes=5)
-gr.Interface(fn=inference, inputs='sketchpad', outputs=outputs, title="MNIST interface",
-             description="Draw a number from 0-9 in the box and click submit to see the model's predictions.").launch()