Update app.py

app.py

@@ -3,34 +3,68 @@ import pickle
 import pandas as pd
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
+from sentence_transformers import SentenceTransformer  # Explicit import
 
-# Load model and data with error handling
+# Load model and data
 try:
+    # Load your trained SentenceTransformer model
     with open("recommender_model.pkl", "rb") as f:
         model = pickle.load(f)
-    posts_df = pd.read_csv("posts_cleaned.csv")
+    
+    # Load posts dataset
+    posts_df = pd.read_csv("posts_cleaned.csv")  
     post_texts = posts_df["post_text"].astype(str).tolist()
-    post_embeddings = np.load("post_embeddings.npy")  # Precomputed
+    
 except Exception as e:
     raise gr.Error(f"Error loading files: {str(e)}")
 
-# Predict function
+# Cache embeddings in memory after first computation
+post_embeddings = None
+
+def get_embeddings():
+    global post_embeddings
+    if post_embeddings is None:
+        print("Computing embeddings for all posts...")
+        post_embeddings = model.encode(post_texts, convert_to_tensor=False)
+        print("Embeddings computed!")
+    return post_embeddings
+
+# Prediction function
 def recommend_from_input(user_text):
     if not user_text.strip():
-        return "Please enter valid text."
+        return []
+    
+    # Get embeddings (computes only once)
+    embeddings = get_embeddings()
+    
+    # Encode user input
     user_vec = model.encode([user_text])
-    sims = cosine_similarity(user_vec, post_embeddings)[0]
-    top_idxs = sims.argsort()[-5:][::-1]  # Top 5 posts
+    
+    # Calculate similarities
+    sims = cosine_similarity(user_vec, embeddings)[0]
+    top_idxs = sims.argsort()[-5:][::-1]  # Top 5 most similar posts
+    
+    # Return as list of strings
     return posts_df.iloc[top_idxs]["post_text"].tolist()
 
-# Gradio UI
+# Gradio Interface
 interface = gr.Interface(
     fn=recommend_from_input,
-    inputs=gr.Textbox(label="Describe your interests"),
-    outputs=gr.Dataframe(headers=["Recommended Posts"]),
-    title="AI Content Recommender",
-    description="Enter a topic or post to get recommendations",
-    examples=[["Web3 security"], ["Machine learning trends"]]
+    inputs=gr.Textbox(label="What are you interested in?", placeholder="e.g. Web3 security tips"),
+    outputs=gr.Dataframe(
+        headers=["Recommended Posts"],
+        datatype=["str"],
+        col_count=(1, "fixed")
+    ),
+    title="🔍 AI Content Recommender",
+    description="Enter a topic or interest to get personalized post recommendations",
+    examples=[
+        ["Blockchain scalability solutions"],
+        ["Latest breakthroughs in AI"],
+        ["How to write smart contracts"]
+    ],
+    allow_flagging="never"
 )
 
-interface.launch()
+# Launch with queue for stability
+interface.launch(share=False)