import os
import gradio as gr
import openai

from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import TokenTextSplitter
from langchain.llms import OpenAI
from langchain.chains import ChatVectorDBChain
from langchain.chains import ConversationalRetrievalChain
from langchain.document_loaders import UnstructuredWordDocumentLoader, TextLoader
from langchain.document_loaders.csv_loader import CSVLoader
from langchain.prompts.prompt import PromptTemplate

os.environ["OPENAI_API_KEY"] = os.getenv("secret1")
openai.api_key = os.getenv("secret1")

def get_document():
    loader1= CSVLoader(file_path='AllProductsTest2.csv')
    data1 = loader1.load()
    loader2= CSVLoader(file_path='BlogLinks.csv')
    data2 = loader2.load()
    return data1+data2

my_data = get_document()

words=["hi", "hii", "hiii", "hey", "heyy", "heyyy", "hello", "helloo", "hellooo", "g morning", "gm", 
       "gmorning", "good morning", "goodmorning", "morning","mornin", "good day", "good afternoon", 
       "good evening", "greetings", "greeting", 
       "good to see you", "its good seeing you", "how are you", "how're you", 
       "how are you doing", "how ya doin'", "how ya doin", "how is everything", 
       "how is everything going", "how's everything going", "how is you", "how's you", 
       "how are things", "how're things", "how is it going", "how's it going", "how's it goin'", 
       "how's it goin", "how is life been treating you", "how's life been treating you", 
       "how have you been", "how've you been", "what is up", "what's up", "what is cracking", 
       "what's cracking", "what is good", "what's good", "what is happening", "what's happening", 
       "what is new", "what's new", "what is neww", "g’day", "howdy",
       "who are you", "who you", "namaste","who r u"]

text_splitter = TokenTextSplitter(chunk_size=500, chunk_overlap=50)
my_doc = text_splitter.split_documents(my_data)

embeddings = OpenAIEmbeddings()
vectordb = Chroma.from_documents(my_doc, embeddings)

greet="""Hello there! 👋 I'm Dr. Aayush, your friendly health partner with wisdom of ayurveda from Kapiva. 😊 Don't hesitate to reach out with any health questions or worries you might have. Whether it's a bothersome symptom, a curiosity about staying healthy, or just a chat about well-being, I'm here to provide you with a caring and understanding ear, infused with the goodness of Ayurveda. 🌼 Your health is important, and with Kapiva, I'm here to support you every step of the way. 💪🏥🌟														
"""

template = open("instruct.txt").read()

QA_PROMPT = PromptTemplate(template=template, input_variables=["question", "context"])

def generate_response(query,chat_history):
    if query:
        llm = OpenAI(temperature=0.7 , model_name="gpt-3.5-turbo")
        my_qa = ChatVectorDBChain.from_llm(llm, vectordb, qa_prompt=QA_PROMPT, return_source_documents=True)
        result = my_qa({"question": query, "chat_history": chat_history})

    return result["answer"]


def my_chatbot(input, history):
    history = history or []
    my_history = list(sum(history, ()))
    my_history.append(input)
    my_input = ' '.join(my_history)
    lowin=input.lower().strip( )
    output=[]
    for el in words:
        if lowin==el:
            output=greet
            if len(history)>0:
                output=output+"\n\nPlease type 'clear' to start a new conversation"
            break
        
    print(output)

    #Clear history
    if lowin=='clear':
        history=[]
        output='Conversation cleared, please state your new query '
        history.append((' ', output))
        history=[] 

    if len((output))==0:
        output = generate_response(input,history)
        output=output+"\n\nPlease type 'clear' to start a new conversation"
    
    print(len(history))
    history.append((input, output))

    if len(history)>15:
        del history[:5]
        
    return history, history

with gr.Blocks() as demo:
    gr.Markdown("""<h1><center>Ayurveda Chatbot Test</center></h1>""")
    chatbot = gr.Chatbot()
    state = gr.State()
    text = gr.Textbox(placeholder="Enter Query")
    text.submit(my_chatbot, inputs=[text, state], outputs=[chatbot, state])
    text.submit(lambda x: gr.update(value=''), [],[text])

demo.launch()