Update app.py

app.py

@@ -4,7 +4,7 @@ import os
 import pandas as pd
 import numpy as np
 from sentence_transformers import SentenceTransformer
-from sklearn.metrics.pairwise_distances_reduction import cosine_similarity_reduction
+from sklearn.metrics.pairwise import cosine_similarity
 import torch
 
 # Set up OpenAI client
@@ -15,8 +15,8 @@ device = "cuda" if torch.cuda.is_available() else "cpu"
 print(f"Using device: {device}")
 
 # Load metadata and embeddings (ensure these files are in your working directory or update paths)
-metadata_path = '/kaggle/working/leetcode_metadata.csv'  # Update this path if needed
-embeddings_path = '/kaggle/working/leetcode_embeddings2.npy'  # Update this path if needed
+metadata_path = 'question_metadata.csv'  # Update this path if needed
+embeddings_path = 'question_dataset_embeddings.npy'  # Update this path if needed
 
 metadata = pd.read_csv(metadata_path)
 embeddings = np.load(embeddings_path)
@@ -45,9 +45,7 @@ def find_top_question(query):
     query_embedding = model.encode(query, convert_to_tensor=True, device=device).cpu().numpy()
     
     # Compute cosine similarity between query embedding and dataset embeddings using scikit-learn's pairwise_distances_reduction
-    similarities = cosine_similarity_reduction(
-        X=query_embedding.reshape(1, -1), Y=embeddings, reduce_func="argmax"
-    )
+    similarities = cosine_similarity(query_embedding, embeddings).flatten()
     
     # Get the index of the most similar result (top 1)
     top_index = similarities.indices[0]  # Index of highest similarity
@@ -64,8 +62,8 @@ def generate_response(prompt):
     st.write(prompt)  # Log the prompt being sent to GPT for debugging
     
     response = client.chat.completions.create(
-        model="gpt-4o",
-        messages=st.session_state.messages + [{"role": "system", "content": prompt}]
+        model="o1-mini",
+        messages=st.session_state.messages + [{"role": "assistant", "content": prompt}]
     )
     return response.choices[0].message.content
 
@@ -103,6 +101,6 @@ if prompt := st.chat_input("Enter a LeetCode-related query (e.g., 'google backtr
 
 st.sidebar.markdown("""
 ## About
-This is a LeetCode to Real-World Interview Question Generator powered by OpenAI's GPT-4.
+This is a LeetCode to Real-World Interview Question Generator powered by OpenAI's API.
 Enter a LeetCode-related query, and it will transform a relevant question into a real-world interview scenario!
 """)