import pandas as pd
import openai
import faiss
import os

# Load your custom CSV data
data = pd.read_csv( os.getcwd() + "\\Learning_Pathway_Index.csv")

# Initialize and populate FAISS index
vector_dimension = 768  # For example, if you use a GPT-3 model with 768-dimensional embeddings
index = faiss.IndexFlatL2(vector_dimension)
vectors = []  # List to store vector representations of data

for text in data['text_column']:
    # Vectorize the text using a pre-trained model (e.g., GPT-3)
    # Replace 'YOUR_OPENAI_API_KEY' with your actual API key
    openai.api_key = os.getenv("OPENAI_API_KEY")
    response = openai.Completion.create(
        engine="text-davinci-002",
        prompt=text,
        max_tokens=50  # Adjust the token limit as needed
    )
    vector = response.choices[0].embedding
    vectors.append(vector)

# Convert the list of vectors to a numpy array
vectors = np.array(vectors).astype('float32')

# Add vectors to the FAISS index
index.add(vectors)

# Accept user questions using OpenAI
user_question = input("Ask a question: ")

# Vectorize the user's question
user_vector = vectorize_user_question(user_question)  # Implement this function

# Search for similar items in the FAISS index
k = 5  # Number of similar items to retrieve
distances, indices = index.search(user_vector, k)

# Retrieve and display the similar items
similar_items = data.iloc[indices[0]]
print(similar_items)