Add application file

app.py

@@ -0,0 +1,82 @@
+import pandas as pd
+import numpy as np
+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+import gradio as gr
+import matplotlib.pyplot as plt
+import matplotlib.image as mpimg
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+classes = { 0:'Speed limit (20km/h)',
+            1:'Speed limit (30km/h)', 
+            2:'Speed limit (50km/h)', 
+            3:'Speed limit (60km/h)', 
+            4:'Speed limit (70km/h)', 
+            5:'Speed limit (80km/h)', 
+            6:'End of speed limit (80km/h)', 
+            7:'Speed limit (100km/h)', 
+            8:'Speed limit (120km/h)', 
+            9:'No passing', 
+            10:'No passing veh over 3.5 tons', 
+            11:'Right-of-way at intersection', 
+            12:'Priority road', 
+            13:'Yield', 
+            14:'Stop', 
+            15:'No vehicles', 
+            16:'Veh > 3.5 tons prohibited', 
+            17:'No entry', 
+            18:'General caution', 
+            19:'Dangerous curve left', 
+            20:'Dangerous curve right', 
+            21:'Double curve', 
+            22:'Bumpy road', 
+            23:'Slippery road', 
+            24:'Road narrows on the right', 
+            25:'Road work', 
+            26:'Traffic signals', 
+            27:'Pedestrians', 
+            28:'Children crossing', 
+            29:'Bicycles crossing', 
+            30:'Beware of ice/snow',
+            31:'Wild animals crossing', 
+            32:'End speed + passing limits', 
+            33:'Turn right ahead', 
+            34:'Turn left ahead', 
+            35:'Ahead only', 
+            36:'Go straight or right', 
+            37:'Go straight or left', 
+            38:'Keep right', 
+            39:'Keep left', 
+            40:'Roundabout mandatory', 
+            41:'End of no passing', 
+            42:'End no passing veh > 3.5 tons' }
+
+
+def transform_images(img):
+    transform = transforms.Compose(
+        [transforms.ToTensor(),
+        transforms.Resize((30, 30)),
+        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
+    )
+    return transform(img)
+
+
+model = torch.jit.load('model_scripted.pt')
+model.eval()
+
+def classify_image(img):
+    image = transform_images(img).to(device)
+    outputs = model(image)
+    _, predicted = torch.max(outputs.data, 1)
+    return classes[int(predicted[0])]
+
+
+image = gr.inputs.Image(shape=(30,30))
+label = gr.outputs.Label()
+examples = ['002_0003_j.png', '054_0024_j.png', '056_1_0001_1_j.png', '003_1_0009_1_j.png', '055_1_0005_1_j.png', '056_1_0013_1_j.png']
+
+intf = gr.Interface(fn=classify_image, inputs=image, outputs=label, examples=examples)
+intf.launch(inline=False)