import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
import openai
import os

# Set up the API key (you can also use st.secrets for security)
OPENAI_API_KEY = st.text_input("OPENAI_API_KEY", type="password")

prompt_file = "prompt_template.txt"

class ProductDescGen(LLMChain):
    """LLM Chain specifically for generating multi-paragraph rich text product description using emojis."""

    @classmethod
    def from_llm(cls, llm: BaseLanguageModel, prompt: str, **kwargs: Any) -> ProductDescGen:
        """Load ProductDescGen Chain from LLM."""
        return cls(llm=llm, prompt=prompt, **kwargs)

def product_desc_generator(product_name, keywords, api_key):
    with open(prompt_file, "r") as file:
        prompt_template = file.read()

    PROMPT = PromptTemplate(input_variables=["product_name", "keywords"], template=prompt_template)
    llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0.7, openai_api_key=api_key)

    ProductDescGen_chain = ProductDescGen.from_llm(llm=llm, prompt=PROMPT)
    ProductDescGen_query = ProductDescGen_chain.apply_and_parse([{"product_name": product_name, "keywords": keywords}])
    return ProductDescGen_query[0]["text"]

st.title("Product Description Generator")

st.write(
    "Generate multi-paragraph rich text product descriptions for your products instantly!"
    " Provide the product name and keywords related to the product."
)

product_name = st.text_input("Product Name", "Nike Shoes")
keywords = st.text_input("Keywords (separated by commas)", "black shoes, leather shoes for men, water resistant")

if st.button("Generate Description"):
    if OPENAI_API_KEY:
        description = product_desc_generator(product_name, keywords, OPENAI_API_KEY)
        st.subheader("Product Description:")
        st.text(description)
    else:
        st.warning("Please provide your OpenAI API Key.")