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import streamlit as st
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline,BitsAndBytesConfig
import os
from langchain_community.llms import HuggingFaceEndpoint
aa=''
HF_TOKEN = os.environ["HF_TOKEN"]
# quants = BitsAndBytesConfig(load_in_4bit=True)
template = ''' You are an expert Blog generator , Given the Topic , the intended audience and the maximum number of words ,
Write a blog on the given topic
Topic : {topic}
Intended Audince : {role}
Number of Words : {words}
Strictly return the output in a markdown format.
Return only the blog and do not provide any other information.'''
prompt = PromptTemplate(template = template,input_variables = ['topic','role','words'])
def main():
st.title(" :fire: Professional Blog Generator :fire:")
st.markdown(
"""
<style>
body {
background-color: #000000;;
color: white;
}
</style>
""",
unsafe_allow_html=True
)
st.sidebar.header("Input Parameters")
role = st.sidebar.text_input("Who is this intednded for ?", "Data Scientist")
topic = st.sidebar.text_input("On what Topic should the blog be on ?", "Machine Learning")
word_count = st.sidebar.slider("Number of Words", min_value=50, max_value=1000, value=200, step=50)
if st.sidebar.button("Generate Blog"):
repo_id = "google/gemma-1.1-7b-it"
llm = HuggingFaceEndpoint(
repo_id=repo_id, max_length=128, temperature=0.5, huggingfacehub_api_token=HF_TOKEN
)
llm_chain = LLMChain(prompt=prompt, llm=llm)
# print(llm_chain.run(question))
aa = llm_chain.run({"topic": topic,"words":word_count,"role":role})
st.write(aa)
# st.write(aa)
# st.write("Will Come here")
if __name__ == "__main__":
main() |