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import streamlit as st
from langchain.prompts import PromptTemplate
from langchain_community.llms import CTransformers
def getLLamaResponse(input_text,no_words,blog_style):
# LLma Model
llm = CTransformers(
model="models/llama-2-7b-chat.ggmlv3.q8_0.bin",
model_type="llma",
config={"max_new_tokens": 256, "temperature": 0.01}
)
# Prompt Template
template = """
Write a blog for {blog_style} job profile for a topic
{input_text} within {no_words} words.
"""
prompt = PromptTemplate(input_variables=["blog_style", "input_text","no_words"],
template=template)
# Generate the response from the LLama 2 Model
response = llm(prompt.format(style=blog_style, text=input_text, no_words=no_words))
print(response)
return response
st.set_page_config(page_title = "Generate Blogs",
page_icon = "🤖",
layout = "centered",
initial_sidebar_state = "collapsed")
st.header("Generate Blogs 🤖")
input_text = st.text_input("Enter the Blog Topic")
# Creating 2 more columns for additional 2 fields
col1, col2 = st.columns([5,5])
with col1:
no_words = st.text_input("No of words")
with col2:
blog_style=st.selectbox("Writing the blog for",
("Researchers","Data Scientist","Common People"),index=0)
submit = st.button("Generate")
# Final Response
if submit:
st.write(getLLamaResponse(input_text,no_words,blog_style))
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