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!
""")