Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+import torch 
+import torch.nn as nn
+import torch.optim as optim 
+import torch.nn.functional as F
+import timm
+from PIL import Image
+from torchvision import transforms
+
+from Models import ResNet, EfficientNet, BaseLine
+
+def get_model(model_name, classes, device):
+
+    if model_name == 'Inception-V3':
+        model = tf.lite.Interpreter(model_path='vgg.tflite')
+        model.allocate_tensors()
+
+    elif model_name == 'VGG':
+        model = tf.lite.Interpreter(model_path='vgg.tflite')
+        model.allocate_tensors()
+
+    elif model_name == 'EfficientNet-B0':
+        model = EfficientNet(len(classes)).to(device)
+        model.load_state_dict(torch.load('EfficientNet-Model.pt'))
+
+    elif model_name == 'ResNet-50':
+        model = ResNet(len(classes)).to(device)
+        model.load_state_dict(torch.load('model-resnet50.pt'))
+
+    elif model_name == 'Base Line Model':
+        model = BaseLine(len(classes)).to(device)
+        model.load_state_dict(torch.load('BaseLine-Model.pt'))
+
+    return model
+
+def make_predictions(input_img, model_name):
+    classes = ['buildings','forest', 'glacier', 'mountain', 'sea', 'street']
+    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+    model = get_model(model_name, classes, device)
+
+    if model_name in ['EfficientNet-B0', 'ResNet-50', 'Base Line Model']:
+        model.eval()
+        img = get_transform(input_img, device)
+        pred = model(img)
+        if torch.cuda.is_available():
+            pred = F.softmax(pred).detach().cpu().numpy()
+            y_prob = pred.argmax(axis=1)[0]
+        else:
+            pred = F.softmax(pred).detach().numpy()
+            y_prob = pred.argmax(axis=1)[0]
+  
+    if model_name in ['Inception-V3', 'VGG']:
+        input_img = np.array(input_img)
+        img = input_img / 255.
+        input_tensor= np.array(np.expand_dims(img,0), dtype=np.float32)
+        input_index = model.get_input_details()[0]["index"]
+        
+        # setting input tensor
+        model.set_tensor(input_index, input_tensor)
+
+        #Run the inference
+        model.invoke()
+        output_details = model.get_output_details()
+
+        # output data of image
+        pred = model.get_tensor(output_details[0]['index'])
+        y_prob = pred.argmax()
+
+    label = classes[y_prob]
+    confidences = {classes[i]: float(pred[0][i]) for i in range(len(classes))}
+    
+    return label, confidences
+
+demo = gr.Interface(
+    fn = make_predictions, 
+    inputs = [gr.Image(shape=(150, 150), type="pil"), gr.Dropdown(choices=['EfficientNet-B0', 'ResNet-50', 'Inception-V3', 'VGG', 'Base Line Model'], value='EfficientNet-B0', label='Choose Model')], 
+    outputs = [gr.outputs.Textbox(label="Output Class"), gr.outputs.Label(label='Confidences')],
+    title = "MultiClass Classifier",
+    examples=[
+              ["Sample_Images/Buildings.jpg", 'EfficientNet-B0'], 
+              ["Sample_Images/Forest.jpg", 'EfficientNet-B0'],
+              ['Sample_Images/Street.jpg', 'EfficientNet-B0'], 
+              ['Sample_Images/glacier.jpg', 'EfficientNet-B0'], 
+              ['Sample_Images/mountain.jpg', 'EfficientNet-B0'], 
+              ['Sample_Images/sea.jpg', 'EfficientNet-B0']
+            ],
+)
+
+demo.launch(debug=True)