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| import streamlit as st | |
| from openai import OpenAI | |
| 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 | |
| import torch | |
| # Set up OpenAI client | |
| client = OpenAI(api_key=os.getenv("OPENAI_API_KEY")) | |
| # Check if GPU is available | |
| 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 = pd.read_csv(metadata_path) | |
| embeddings = np.load(embeddings_path) | |
| # Load the SentenceTransformer model | |
| model = SentenceTransformer("all-MiniLM-L6-v2").to(device) | |
| # Load the system prompt from the file | |
| with open("prompt.txt", "r") as file: | |
| system_prompt = file.read() | |
| st.title("LeetCode to Real-World Interview Question Generator") | |
| # Initialize chat history | |
| if "messages" not in st.session_state: | |
| st.session_state.messages = [{"role": "assistant", "content": system_prompt}] | |
| # Display chat messages from history on app rerun | |
| for message in st.session_state.messages[1:]: # Skip the system message | |
| with st.chat_message(message["role"]): | |
| st.markdown(message["content"]) | |
| # Function to find the top 1 most similar question based on user input | |
| def find_top_question(query): | |
| # Generate embedding for the 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" | |
| ) | |
| # Get the index of the most similar result (top 1) | |
| top_index = similarities.indices[0] # Index of highest similarity | |
| # Retrieve metadata for the top result | |
| top_result = metadata.iloc[top_index].copy() | |
| top_result['similarity_score'] = similarities.distances[0] | |
| return top_result | |
| # Function to generate response using OpenAI API with debugging logs | |
| def generate_response(prompt): | |
| st.write("### Debugging Log: Data Sent to GPT") | |
| 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}] | |
| ) | |
| return response.choices[0].message.content | |
| # React to user input | |
| if prompt := st.chat_input("Enter a LeetCode-related query (e.g., 'google backtracking'):"): | |
| # Display user message in chat message container | |
| st.chat_message("user").markdown(prompt) | |
| # Add user message to chat history | |
| st.session_state.messages.append({"role": "user", "content": prompt}) | |
| # Find the top question based on user input | |
| top_question = find_top_question(prompt) | |
| # Prepare a detailed prompt for GPT using the top question's details | |
| detailed_prompt = ( | |
| f"Transform this LeetCode question into a real-world interview scenario:\n\n" | |
| f"**Company**: {top_question['company']}\n" | |
| f"**Question ID**: {top_question['questionId']}\n" | |
| f"**Question Name**: {top_question['questionName']}\n" | |
| f"**Difficulty Level**: {top_question['difficulty level']}\n" | |
| f"**Tags**: {top_question['Tags']}\n" | |
| f"**Content**: {top_question['Content']}\n" | |
| f"\nPlease create a real-world interview question based on this information." | |
| ) | |
| # Generate response using GPT-4 with detailed prompt and debugging logs | |
| response = generate_response(detailed_prompt) | |
| # Display assistant response in chat message container | |
| with st.chat_message("assistant"): | |
| st.markdown(response) | |
| # Add assistant response to chat history | |
| st.session_state.messages.append({"role": "assistant", "content": response}) | |
| st.sidebar.markdown(""" | |
| ## About | |
| This is a LeetCode to Real-World Interview Question Generator powered by OpenAI's GPT-4. | |
| Enter a LeetCode-related query, and it will transform a relevant question into a real-world interview scenario! | |
| """) |