Update app.py

app.py

@@ -2,13 +2,16 @@ import gradio as gr
 import pickle
 import pandas as pd
 from sklearn.metrics.pairwise import cosine_similarity
+from sklearn.metrics.pairwise import cosine_similarity
 
 # Load model and dataset
 with open("recommender_model.pkl", "rb") as f:
     model = pickle.load(f)
 
 posts_df = pd.read_csv("posts_cleaned.csv")  # your full dataset with post content
-post_embeddings = model["embeddings"]  # precomputed post embeddings
+post_texts = posts["post_text"].astype(str).tolist()
+post_embeddings = model.encode(post_texts, convert_to_tensor=False)
+  # precomputed post embeddings
 vectorizer = model["vectorizer"]      # for transforming user input
 
 # Predict function