Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import pickle
+
+from torch_geometric.nn import GCNConv, LGConv
+from torch_geometric.utils import degree
+from torch_geometric.nn.conv import MessagePassing
+from torch_geometric.data import HeteroData, Data
+import  torch_geometric.transforms as T
+from torch_geometric.nn import LightGCN
+import utils
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+data = torch.load("processed_MVL_light.pt")
+ch = torch.load('./lightGCNModel_num_layers_MovieLens100K_checkpoint.pt')
+lightGCNModel = LightGCN(
+        num_nodes=data.num_nodes,
+        embedding_dim=64,
+        num_layers=3,
+    ).to(device)
+optimizer = torch.optim.Adam(lightGCNModel.parameters(), lr=0.005)
+mask_train = data.edge_index[0] < data.edge_index[1]
+train_edge_label_index = data.edge_index[:, mask_train]
+lightGCNModel.load_state_dict(ch['model_state_dict'])
+optimizer.load_state_dict(ch['optimizer_state_dict'])
+num_items = 1682
+num_users = 943
+
+def recommend(user_id):
+
+    ground_truth_items, recommendations = utils.predict(lightGCNModel, device, data, num_users, num_items, user_id, train_edge_label_index, k=5)
+    return ground_truth_items, recommendations
+
+iface = gr.Interface(fn=recommend, inputs="number", outputs=["text", "text"])
+iface.launch()