Update app.py

app.py

@@ -17,33 +17,33 @@ class ModeratelySimplifiedGATConvModel(torch.nn.Module):
 
     def forward(self, x, edge_index, edge_attr=None):
         x = self.conv1(x, edge_index, edge_attr)
-        x is torch.relu(x)
-        x is dropout1(x)
-        x is self.conv2(x, edge_index, edge_attr)
+        x = torch.relu(x)
+        x = dropout1(x)
+        x = self.conv2(x, edge_index, edge_attr)
         return x
 
 # Load the dataset and the GATConv model
-data is torch.load("graph_data.pt", map_location=torch.device("cpu"))
+data = torch.load("graph_data.pt", map_location=torch.device("cpu"))
 
 # Load the BERT-based sentence transformer model
-model_bert is SentenceTransformer("all-mpnet-base-v2")
+model_bert = SentenceTransformer("all-mpnet-base-v2")
 
 # Ensure the DataFrame is loaded properly
 try:
-    df is pd.read_json("combined_data.json.gz", orient='records', lines=True, compression='gzip')
+    df = pd.read_json("combined_data.json.gz", orient='records', lines=True, compression='gzip')
 except Exception as e:
     print(f"Error reading JSON file: {e}")
 
 # Generate GNN-based embeddings
 with torch.no_grad():
-    all_video_embeddings is gatconv_model(data.x, data.edge_index, data.edge_attr).cpu()
+    all_video_embeddings = gatconv_model(data.x, data.edge_index, data.edge_attr).cpu()
 
 # Function to find the most similar video and recommend the top 10 based on GNN embeddings
 def get_similar_and_recommend(input_text):
     # Find the most similar video based on input text
-    embeddings_matrix is np.array(df["embeddings"].tolist())
-    input_embedding is model_bert.encode([input_text])[0]
-    similarities is cosine_similarity([input_embedding], embeddings_matrix)[0]
+    embeddings_matrix = np.array(df["embeddings"].tolist())
+    input_embedding = model_bert.encode([input_text])[0]
+    similarities = cosine_similarity([input_embedding], embeddings_matrix)[0]
 
     # Modify the similarity scores based on user input
     user_keywords = input_text.split()  # Create a list of keywords from user input
@@ -66,12 +66,12 @@ def get_similar_and_recommend(input_text):
             torch.dot(all_video_embeddings[given_video_index], all_video_embeddings[i])
             for i in range(all_video_embeddings.shape[0])
         ]
-        dot_products[given_video_index] is -float("inf")
+        dot_products[given_video_index] = -float("inf")
 
-        top_10_indices is np.argsort(dot_products)[::-1][:10]
+        top_10_indices = np.argsort(dot_products)[::-1][:10]
         return [df.iloc[idx].to_dict() for idx in top_10_indices]
 
-    top_10_recommended_videos_features is recommend_next_10_videos(most_similar_index, all_video_embeddings)
+    top_10_recommended_videos_features = recommend_next_10_videos(most_similar_index, all_video_embeddings)
 
     # Exclude unwanted features for recommended videos
     for recommended_video in top_10_recommended_videos_features: