import openai
import pandas as pd
import numpy as np
import faiss
import os
from dotenv import load_dotenv
from sklearn.metrics.pairwise import cosine_similarity

load_dotenv()  # take environment variables from .env.
api_key = os.getenv('OPENAI_API_KEY')
#print(api_key)


from openai import OpenAI
client = OpenAI()

def get_openai_embedding(text, model="text-embedding-3-small"):
   text = text.replace("\n", " ")
   return client.embeddings.create(input = [text], model=model).data[0].embedding


def save_openai_embeddings(csv_file, model='text-embedding-3-small'):
    # Load the CSV file
    df = pd.read_csv(csv_file)
    # Save the embeddings
    df['ada_embeddings'] = df.utterance.apply(lambda x: get_openai_embedding(x, model='text-embedding-3-small'))
    df.to_csv('embeddings/openai_embeddings.csv', index=False)
    print(f"Embeddings saved to embeddings/openai_embeddings.csv.")

# get and save the embeddings for Intent cleared data

#save_openai_embeddings(r'C:\Users\ZZ029K826\Documents\GitHub\LLM_Intent_Recognition\data\Pager_Intents_cleaned.csv')


def load_openai_embeddings(csv_file):
    # Load the CSV file
    df = pd.read_csv(csv_file)
    # Extract the embeddings
    embeddings = df['ada_embeddings'].tolist()
    return embeddings


# Function to calculate similarity between user input and precomputed embeddings
def calculate_openai_similarity(user_text, df, top_n=5):
    # Get embedding for the user input text
    user_embedding = np.array(get_openai_embedding(user_text, model='text-embedding-3-small')).reshape(1, -1)
    
    # Calculate cosine similarity between user input and all precomputed embeddings
    df['similarity'] = df['ada_embedding'].apply(lambda x: cosine_similarity([x], user_embedding)[0][0])
    
    # Sort by similarity score (descending) and return the top_n most similar
    top_matches = df.sort_values(by='similarity', ascending=False).head(top_n)
    
    return top_matches[['combined', 'similarity']]

def get_openai_similarity(user_text, df, top_n=5):
    # Get embedding for the user input text
    user_embedding = np.array(get_openai_embedding(user_text, model='text-embedding-3-small')).reshape(1, -1)
    
    # Calculate cosine similarity between user input and all precomputed embeddings
    df['similarity'] = df['ada_embedding'].apply(lambda x: cosine_similarity([x], user_embedding)[0][0])
    
    # Sort by similarity score (descending) and return the top_n most similar
    top_matches = df.sort_values(by='similarity', ascending=False).head(top_n)
    
    return top_matches[['combined', 'similarity']]