from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
from llama_index import download_loader
from pandasai.llm.openai import OpenAI
from matplotlib import pyplot as plt
import streamlit as st
import pandas as pd
import os


documents_folder = "./documents"

# Load PandasAI loader, Which is a wrapper over PandasAI library
PandasAIReader = download_loader("PandasAIReader")

st.title("Welcome to `RegBot5`")
st.header("Interact with Documents such as `PDFs/CSV/Docs` using the power of LLMs\nPowered by `LlamaIndex🦙` \nCheckout the [GITHUB Repo Here](https://github.com/anoopshrma/Chat-with-Docs) and Leave a star⭐")


def get_csv_result(df, query):
  reader = PandasAIReader(llm=csv_llm)
  response = reader.run_pandas_ai(
    df, 
    query, 
    is_conversational_answer=False
    )
  return response

def save_file(doc):        
    fn = os.path.basename(doc.name)
    # open read and write the file into the server
    open(documents_folder+'/'+fn, 'wb').write(doc.read())
    # Check for the current filename, If new filename
    # clear the previous cached vectors and update the filename 
    # with current name     
    if st.session_state.get('file_name'):
        if st.session_state.file_name != fn:
            st.cache_resource.clear()
            st.session_state['file_name'] = fn
    else:
        st.session_state['file_name'] = fn

    return fn

def remove_file(file_path):
    # Remove the file from the Document folder once 
    # vectors are created
    if os.path.isfile(documents_folder+'/'+file_path):
        os.remove(documents_folder+'/'+file_path)

    

@st.cache_resource
def create_index():
    # Create vectors for the file stored under Document folder. 
    # NOTE: You can create vectors for multiple files at once.
    documents = SimpleDirectoryReader(documents_folder).load_data()
    index = GPTVectorStoreIndex.from_documents(documents)
    return index



def query_doc(vector_index, query):
    # Applies Similarity Algo, Finds the nearest match and 
    # take the match and user query to OpenAI for rich response
    query_engine = vector_index.as_query_engine()
    response = query_engine.query(query)
    return response


api_key = st.text_input("Enter your OpenAI API key here:", type="password")
if api_key:
    os.environ['OPENAI_API_KEY'] = api_key
    csv_llm = OpenAI(api_token=api_key)


tab1, tab2= st.tabs(["CSV", "PDFs/Docs"])

with tab1:
   
   st.write("Chat with CSV files using PandasAI loader with LlamaIndex")
   input_csv = st.file_uploader("Upload your CSV file", type=['csv'])
   
   if input_csv is not None:      
    st.info("CSV Uploaded Successfully")
    df = pd.read_csv(input_csv)
    st.dataframe(df, use_container_width=True)
    

   st.write("---")
   
   input_text = st.text_area("Ask your query")
   
   if input_text is not None:
    if st.button("Send"):
      st.info("Your query: "+ input_text)
      with st.spinner('Processing your query...'):
        response = get_csv_result(df, input_text)
      if plt.get_fignums():
          st.pyplot(plt.gcf())
      else:
        st.success(response)

    
with tab2:
   st.write("Chat with PDFs/Docs")
   input_doc = st.file_uploader("Upload your Docs")
   
   if input_doc is not None: 
       st.info("Doc Uploaded Successfully")
       file_name = save_file(input_doc)
       index = create_index()
       remove_file(file_name)
       
   
   st.write("---")
   input_text = st.text_area("Ask your question")
   
   if input_text is not None:
    if st.button("Ask"):
        st.info("Your query: \n" +input_text)
        with st.spinner("Processing your query.."):
            response = query_doc(index, input_text)
            print(response)
        
        st.success(response)

        st.write("---")
        # Shows the source documents context which 
        # has been used to prepare the response
        st.write("Source Documents")
        st.write(response.get_formatted_sources())