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| import streamlit as st | |
| from langchain.prompts import PromptTemplate | |
| from langchain.llms import CTransformers | |
| def getLLamaresponse(input_text, no_words, blog_style): | |
| # Initialize the language model with the specified configuration | |
| # Use a pipeline as a high-level helper | |
| from transformers import pipeline | |
| llm = pipeline("text-generation", model="TheBloke/Llama-2-7B-Chat-GGML") | |
| # Define the template for the prompt | |
| template = """ | |
| Write a blog in {blog_style} job profile for the topic "{input_text}" within {no_words} words. | |
| """ | |
| # Create the prompt using the template | |
| prompt = PromptTemplate(input_variables=['blog_style', 'input_text', 'no_words'], template=template) | |
| # Format the prompt with the provided parameters | |
| formatted_prompt = prompt.format(blog_style=blog_style, input_text=input_text, no_words=no_words) | |
| # Generate the response using the language model | |
| response = llm(formatted_prompt) | |
| return response | |
| # Configure the Streamlit app | |
| st.set_page_config(page_title="Generate Blog", | |
| layout='centered', | |
| initial_sidebar_state='collapsed') | |
| # App header | |
| st.header("Generate Blog") | |
| # Input for the blog topic | |
| input_text = st.text_input("Enter the blog topic") | |
| # Columns for additional inputs | |
| col1, col2 = st.columns([5, 5]) | |
| with col1: | |
| no_words = st.text_input('Number of words') | |
| with col2: | |
| blog_style = st.selectbox('Writing the blog for', ('researchers', 'data scientists', 'common people'), index=0) | |
| # Generate button | |
| submit = st.button("Generate") | |
| # Display the generated response | |
| if submit: | |
| response = getLLamaresponse(input_text, no_words, blog_style) | |
| st.write(response) | |