Update app.py

app.py

@@ -1,5 +1,81 @@
 import gradio as gr
-import predict as predict
+#Import libraries
+import torch
+import torchvision.models as models
+import json
 
-iface = gr.Interface(fn=predict, inputs=gr.inputs.Image(type='pil'), outputs=gr.outputs.Label(num_top_classes=3))
+#Import User Defined libraries
+from neural_network_model import initialize_existing_models, build_custom_models, set_parameter_requires_grad
+from utilities import process_image, get_input_args_predict
+
+def predict(image_path, model, topk=5):
+    
+    
+    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    model.eval()
+    
+    tensor_img = torch.FloatTensor(process_image(image_path))
+    tensor_img = tensor_img.unsqueeze(0)
+    tensor_img = tensor_img.to(device)
+    log_ps = model(tensor_img)
+    result = log_ps.topk(topk)
+    if torch.cuda.is_available(): #gpu Move it from gpu to cpu for numpy
+        ps = torch.exp(result[0].data).cpu().numpy()[0] 
+        idxs = result[1].data.cpu().numpy()[0]
+    else: #cpu Keep it on cpu for nump
+        ps = torch.exp(result[0].data).numpy()[0]
+        idxs = result[1].data.numpy()[0]
+
+    return (ps, idxs)
+    
+def process_input(image_path):
+    #0. Get user inputs
+    #in_arg = vars(get_input_args_predict())
+    #print("User arguments/hyperparameters or default used are as below")
+    #print(in_arg)
+    in_arg = {}
+    in_arg['gpu'] = 'gpu'
+    in_arg['save_dir'] = 'checkpoint-densenet121.pth'
+    in_arg['path'] = image_path
+    in_arg['top_k'] = 5
+    
+    #1. Get device for prediction and Load model from checkpoint along with some other information
+    if in_arg['gpu'] == 'gpu' and torch.cuda.is_available():
+        device = torch.device("cuda")
+        checkpoint = torch.load(in_arg['save_dir'])
+    else:
+        device = "cpu"
+        checkpoint = torch.load(in_arg['save_dir'], map_location = device)
+    #print(f"Using {device} device for predicting/inference")
+    
+    if checkpoint['arch_type'] == 'existing':
+        model_ft, input_size = initialize_existing_models(checkpoint['arch'], checkpoint['arch_type'], len(checkpoint['class_to_idx']),
+                                                          checkpoint['feature_extract'], checkpoint['hidden_units'], use_pretrained=False)
+    elif checkpoint['arch_type'] == 'custom':
+        model_ft = build_custom_models(checkpoint['arch'], checkpoint['arch_type'], len(checkpoint['class_to_idx']), checkpoint['feature_extract'], 
+                                       checkpoint['hidden_units'], use_pretrained=True)
+    else:
+        #print("Nothing to predict")
+        exit()
+        
+       
+    model_ft.class_to_idx = checkpoint['class_to_idx']
+    model_ft.gpu_or_cpu = checkpoint['gpu_or_cpu']
+    model_ft.load_state_dict(checkpoint['state_dict'])
+    model_ft.to(device)
+    
+    #Predict
+    # Get the prediction by passing image and other user preferences through the model
+    probs, idxs  = predict(image_path = in_arg['path'], model = model_ft, topk = in_arg['top_k'])
+    
+    # Swap class to index mapping with index to class mapping and then map the classes to flower category labels using the json file
+    idx_to_class = {v: k for k, v in model_ft.class_to_idx.items()}
+    with open('cat_to_name.json','r') as f:
+        cat_to_name = json.load(f)
+    names = list(map(lambda x: cat_to_name[f"{idx_to_class[x]}"],idxs))
+    
+    return names, probs
+
+iface = gr.Interface(fn=process_input, inputs=gr.inputs.Image(type='pil'), outputs=gr.outputs.Label(num_top_classes=3))
 iface.launch()