content.py

from __future__ import annotations
import langchain
import vertexai
from vertexai.language_models import TextGenerationModel
import streamlit as st
from langchain_community.llms import VertexAI
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatVertexAI
from typing import Any
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from langchain.embeddings import VertexAIEmbeddings
import os

os.environ['GOOGLE_APPLICATION_CREDENTIALS']="agileai-poc-10f5fe13f8a2.json"
model = TextGenerationModel.from_pretrained("text-bison@001")
# project_id = "agileai-poc"
# loc = "us-central1"
# vertexai.init(project=project_id, location=loc)
# params = VertexAI(
#     model_name="text-bison@001",
#     max_output_tokens=256,
#     temperature=0.2,
#     top_p=0.8
# )
prompt="modify the text and highlight the points of the given input which type of tone it contains "
# class txt_gen(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
#     ) -> txt_gen:
#         """Load txt_gen Chain from LLM."""
#         return cls(llm=params, prompt=prompt, **kwargs)


# def generate_text(input):
#     with open(prompt, "r") as file:
#         prompt_template = file.read()

#     PROMPT = PromptTemplate(
#         input_variables=[input], template=prompt_template
#     )

#     DescGen_chain = txt_gen.from_llm(llm=params, prompt=PROMPT)
#     DescGen_query = DescGen_chain.apply_and_parse(
#         [{"input":input}]
#     )
#     return DescGen_query[0]["text"]
c1,c2,c3=st.columns(3)
with c1:
    input=st.text_input("Enter your content :")
submit=st.button("Submit")
if submit:
    # description = st.write(generate_text(input))
    desc=st.write(model.predict(prompt))
#     print(model.predict(prompt))
# with c3:
#     output=st.write(description)