from dotenv import load_dotenv
import os

from llama_index.core import download_loader
from llama_index.readers.file import CSVReader
from llama_index.readers.file import PagedCSVReader
from pathlib import Path
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core import Settings
from llama_index.core import VectorStoreIndex
import openai
filepath="part1.csv"


load_dotenv()
openai.api_key = os.getenv("OPENAI_API_TOKEN")
#embed_model = OpenAIEmbedding()
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
llm=OpenAI(model="gpt-3.5-turbo-instruct")

Settings.embed_model=embed_model
Settings.llm=llm

CSVReader = download_loader("PagedCSVReader")

loader = PagedCSVReader()
documents = loader.load_data(file=Path(filepath))

index = VectorStoreIndex.from_documents(documents)


def chatbot_function(input_text):
    
    query_engine = index.as_query_engine(chat_mode="condense_question", verbose=True)
    response = query_engine.query(input_text)
    #response.print_response_stream()
    return response.response

response = chatbot_function("What kind of reviews are given by majority of employees")
print(response)