#!pip install python-dotenv
#pip install duckdb
#####
import duckdb
#pip install llama_index_core
import os
#pip install openai
import openai
import textwrap 
#pip install llama_index.vector_stores.duckdb
import llama_index.vector_stores.duckdb
#pip install llama-index-embeddings-openai
#import llama-index-embeddings-openai
import os 
import openai
import textwrap 



file_path = 'persist/my_vector_store.duckdb'

# Check if file exists
if os.path.exists(file_path):
  #Delete the file
  os.remove(file_path)
  print("File deleted successfully")
else:
  print("File doesn't exist - first run - it's all good")

from dotenv import load_dotenv
load_dotenv()

api_key = os.getenv('OPENAI_API_KEY')

from openai import OpenAI
client = OpenAI(api_key=api_key)


print("OpenAI key generated")


default_model = os.environ.get('OPENAI_DEFAULT_MODEL')

if default_model:
	print(f"The default OpenAI model (from environment variable) is: {default_model}")
else:
  print("The OPENAI_DEFAULT_MODEL environment variable is not set.")



from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.duckdb import DuckDBVectorStore
from llama_index.core import StorageContext

print("Vector Stores Generated")


# Initialize DuckDB vector store with persistence
vector_store = DuckDBVectorStore("my_vector_store.duckdb", persist_dir="./persist/")


print("Initialized DuckDB vector store with peristance")

documents = SimpleDirectoryReader("PDFs").load_data()


print("PDFs loaded into Duckdb")

storage_context = StorageContext.from_defaults(vector_store=vector_store)

print("storage_context defined")

index = VectorStoreIndex.from_documents(documents, storage_context=storage_context)

print("Index generated from vector stores in DuckDB")



import gradio as gr

# Create a custom theme with blue as the primary color
theme = gr.themes.Default(primary_hue="blue")  


def greet(query):
    
    query_engine = index.as_query_engine()
    response = query_engine.query(query)
    strresponse = str(response)
    #return(gradio.Markdown(strresponse))
    #return(textwrap.fill(str(response), 80))
    return(f"{response}")
    #display(Markdown(f"<b>{response}</b>")
    #return "Hello " + query + "!"

demo = gr.Interface(fn=greet, inputs="text", outputs="text")
demo.launch(share=True)