Delete app.py with huggingface_hub

app.py

@@ -1,91 +0,0 @@
-import os
-from os.path import splitext
-import numpy as np
-import sys
-import matplotlib.pyplot as plt
-import torch
-import torchvision
-import wget 
-
-
-destination_folder = "output"
-destination_for_weights = "weights"
-
-if os.path.exists(destination_for_weights):
-    print("The weights are at", destination_for_weights)
-else:
-    print("Creating folder at ", destination_for_weights, " to store weights")
-    os.mkdir(destination_for_weights)
-    
-segmentationWeightsURL = 'https://github.com/douyang/EchoNetDynamic/releases/download/v1.0.0/deeplabv3_resnet50_random.pt'
-
-if not os.path.exists(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL))):
-    print("Downloading Segmentation Weights, ", segmentationWeightsURL," to ",os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))
-    filename = wget.download(segmentationWeightsURL, out = destination_for_weights)
-else:
-    print("Segmentation Weights already present")
-
-torch.cuda.empty_cache()
-
-def collate_fn(x):
-    x, f = zip(*x)
-    i = list(map(lambda t: t.shape[1], x))
-    x = torch.as_tensor(np.swapaxes(np.concatenate(x, 1), 0, 1))
-    return x, f, i
-
-model = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, aux_loss=False)
-model.classifier[-1] = torch.nn.Conv2d(model.classifier[-1].in_channels, 1, kernel_size=model.classifier[-1].kernel_size)
-
-print("loading weights from ", os.path.join(destination_for_weights, "deeplabv3_resnet50_random"))
-
-if torch.cuda.is_available():
-    print("cuda is available, original weights")
-    device = torch.device("cuda")
-    model = torch.nn.DataParallel(model)
-    model.to(device)
-    checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)))
-    model.load_state_dict(checkpoint['state_dict'])
-else:
-    print("cuda is not available, cpu weights")
-    device = torch.device("cpu")
-    checkpoint = torch.load(os.path.join(destination_for_weights, os.path.basename(segmentationWeightsURL)), map_location = "cpu")
-    state_dict_cpu = {k[7:]: v for (k, v) in checkpoint['state_dict'].items()}
-    model.load_state_dict(state_dict_cpu)
-
-model.eval()
-
-def segment(input):
-    inp = input
-    x = inp.transpose([2, 0, 1])  #  channels-first
-    x = np.expand_dims(x, axis=0)  # adding a batch dimension    
-    
-    mean = x.mean(axis=(0, 2, 3))
-    std = x.std(axis=(0, 2, 3))
-    x = x - mean.reshape(1, 3, 1, 1)
-    x = x / std.reshape(1, 3, 1, 1)
-    
-    with torch.no_grad():
-        x = torch.from_numpy(x).type('torch.FloatTensor').to(device)
-        output = model(x)    
-    
-    y = output['out'].numpy()
-    y = y.squeeze()
-    
-    out = y>0    
-    
-    mask = inp.copy()
-    mask[out] = np.array([0, 0, 255])
-    
-    return mask
-
-import gradio as gr
-
-i = gr.inputs.Image(shape=(112, 112), label="Echocardiogram")
-o = gr.outputs.Image(label="Segmentation Mask")
-
-examples = [["img1.jpg"], ["img2.jpg"]]
-title = None #"Left Ventricle Segmentation"
-description = "This semantic segmentation model identifies the left ventricle in echocardiogram images."
-# videos. Accurate evaluation of the motion and size of the left ventricle is crucial for the assessment of cardiac function and ejection fraction. In this interface, the user inputs apical-4-chamber images from echocardiography videos and the model will output a prediction of the localization of the left ventricle in blue. This model was trained on the publicly released EchoNet-Dynamic dataset of 10k echocardiogram videos with 20k expert annotations of the left ventricle and published as part of ‘Video-based AI for beat-to-beat assessment of cardiac function’ by Ouyang et al. in Nature, 2020."
-thumbnail = "https://raw.githubusercontent.com/gradio-app/hub-echonet/master/thumbnail.png"
-gr.Interface(segment, i, o, examples=examples, allow_flagging=False, analytics_enabled=False, thumbnail=thumbnail, cache_examples=False).launch()