Update app.py

app.py

@@ -1,4 +1,6 @@
 import gradio as gr
+from fastai.collab import *
+from fastai.tabular.all import *
 from fastai.learner import load_learner
 import pandas as pd
 import numpy as np
@@ -11,6 +13,36 @@ ids_list = list(map(str, ids.tolist()))
 
 ratings = pd.read_csv('ratings.csv')
 
+class CollabNN(Module):
+    def __init__(self, user_sz, item_sz, y_range=(0,5.5), n_act=100):
+        self.user_factors = Embedding(*user_sz)
+        self.item_factors = Embedding(*item_sz)
+        self.layers = nn.Sequential(
+            nn.Linear(user_sz[1]+item_sz[1], n_act),
+            nn.ReLU(),
+            nn.Linear(n_act, 1))
+        self.y_range = y_range
+        
+    def forward(self, x):
+        embs = self.user_factors(x[:,0]),self.item_factors(x[:,1])
+        x = self.layers(torch.cat(embs, dim=1))
+        return sigmoid_range(x, *self.y_range)
+
+class DotProduct(Module):
+    def __init__(self, n_users, n_books, n_factors, y_range=(0, 5.5)):
+        self.user_factors = Embedding(n_users, n_factors)
+        self.user_bias = Embedding(n_users, 1)
+        self.books_factors = Embedding(n_books, n_factors)
+        self.books_bias = Embedding(n_books, 1)
+        self.y_range = y_range
+    
+    def forward(self, x):
+        users = self.user_factors(x[:,0])
+        books = self.books_factors(x[:,1])
+        res = (users * books).sum(dim=1, keepdim=True)
+        res += self.user_bias(x[:,0]) + self.books_bias(x[:,1])
+        return sigmoid_range(res, *self.y_range)
+
 def top5(user):
     user = int(user)
     items = pd.Series(learn.dls.classes['title']).unique()