import gradio as gr
from langchain_openai import OpenAIEmbeddings, ChatOpenAI
from pinecone import Pinecone
from langchain.chains import RetrievalQAWithSourcesChain
from langchain_community.vectorstores import Pinecone as PineconeVec
import os

openai_key = os.environ['openai_key']
pinecone_key = os.environ['pinecone_key']

index_name = 'first-aid'

embedding_model_name = 'text-embedding-ada-002'

embed = OpenAIEmbeddings(
    model=embedding_model_name,
    openai_api_key=openai_key
)

pc = Pinecone(api_key=pinecone_key)
index =  pc.Index(index_name)
text_field = "text"
vectorstore = PineconeVec(
    index,
    embed,
    text_field
)

llm = ChatOpenAI(
    openai_api_key=openai_key,
    model_name='gpt-3.5-turbo',
    temperature=0.0
)

qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type(
    llm=llm,
    chain_type="stuff",
    retriever=vectorstore.as_retriever()
)

def answer_question(query):
    result = qa_with_sources.invoke(query)
    return result['answer'] + 'sources: {}'.format(result['sources'])


iface = gr.Interface(
    fn=answer_question,
    inputs="text",
    outputs="text"
)
iface.launch(share=True)
#%%