app.py

import gradio as gr
import pinecone
import openai
import os
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.chains import ConversationalRetrievalChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
from langchain.vectorstores import Pinecone 
from langchain.prompts.prompt import PromptTemplate 



BOOK_TOKEN = os.getenv("book")
pine = os.getenv("pine")
HF_TOKEN = os.getenv("HF_TOKEN")

os.environ["OPENAI_API_KEY"] = BOOK_TOKEN

OPENAI_API_KEY = ""
PINECONE_API_KEY = ""
PINECONE_API_ENV = "gcp-starter"

#embedding = OpenAIEmbeddings(openai_api_key=OPENAI_API_KEYs)
embed_model = "text-embedding-ada-003"

pinecone.init(
    api_key=pine,
    environment=PINECONE_API_ENV
)
openai.api_key=BOOK_TOKEN
index_n = "hospital-code"
index = pinecone.Index(index_n)
index.describe_index_stats()

limit = 3750

llm = ChatOpenAI(temperature=0, model_name="gpt-4" )

embeddings = OpenAIEmbeddings(
    model="text-embedding-3-large"
)

#get the db index
db = Pinecone.from_existing_index(index_name=index_n, embedding=embeddings)

theme = gr.themes.Soft(
    primary_hue="emerald",
).set(
    block_background_fill='black')


with gr.Blocks(theme=theme) as demo:
    chatbot = gr.Chatbot(label="Talk to the Bot", show_copy_button=True, show_label=True)
    msg = gr.Textbox()
    clear = gr.Button("Clear")
    chat_history = []

    def vote(data: gr.LikeData):
        if data.liked:
            print("You upvoted this response: " + data.value)
        else:
            print("You downvoted this reposnse: " + data.value)
    
    def user(user_message, chat_history):

        memory = ConversationBufferMemory(
            memory_key='chat_history',
            return_messages=False
        )

        #PUT IT IN A PROMPT TEMPLATE
        #template = """The following is chat between a human and an AI assistant. The AI provides the answer along with the section it referred to for the answer.
        #Current Conversation:
        #{history}
        #Friend: {input}
        #AI:
        #"""
        #PROMPT = PromptTemplate(input_variables=["history", "input"], template=template)
    
        #Initalize lanchain - Conversation Retrieval Chain
        qa = ConversationalRetrievalChain.from_llm(ChatOpenAI(temperature=0), retriever=db.as_retriever(), memory=memory)

    

        #get response from QA Chain
        response = qa({'question': user_message, "chat_history": chat_history})
        #append user message and respone to chat history
        chat_history.append((user_message, response["answer"]))
        return gr.update(value=""), chat_history
    msg.submit(user, [msg, chatbot], [msg, chatbot], queue=False)
    chatbot.like(vote, None, None)
    clear.click(lambda: None, None, chatbot, queue=False)

if __name__ == "__main__":
  demo.launch(debug=True)