app.py

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='llama',
                      config={'max_new_tokens':256,
                              'temperature':0.01})
    
    # Prompt Template
    # 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(blog_style=blog_style, input_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))