app.py

import streamlit as st
from langchain.prompts import PromptTemplate
from langchain_community.llms import CTransformers
import time

# List of blog styles
BLOG_STYLES = [
    'Researchers', 
    'Data Scientist', 
    'Common People', 
    'Software Engineers', 
    'Product Managers', 
    'Healthcare Professionals', 
    'Teachers', 
    'Entrepreneurs', 
    'Marketers', 
    'Students'
]

# Function to get response from LLama 2 model
def getLLamaResponse(input_text, no_words, blog_style):
    # Initialize the LLama 2 model
    llm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin',
                        model_type='llama',
                        config={'max_new_tokens': 256, 'temperature': 0.01})

    # Define the 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))
    return response

# Function to generate topics from LLama 2 model
def generate_topics_from_llama(input_text):
    # Initialize the LLama 2 model
    llm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin',
                        model_type='llama',
                        config={'max_new_tokens': 256, 'temperature': 0.01})

    # Define the prompt template for generating topics
    topic_template = """
    Generate a list of blog topics based on the keywords: {input_text}
    """
    prompt = PromptTemplate(input_variables=["input_text"], template=topic_template)
    
    # Generate the topics from the LLama 2 model
    topics_response = llm(prompt.format(input_text=input_text))
    # Split the response into a list of topics
    topics = topics_response.split('\n')
    return [topic.strip() for topic in topics if topic.strip()]

# Set up the Streamlit page configuration
st.set_page_config(page_title="LLAMA 2 Generate Blogs",
                   page_icon='images/favicon.ico',  # Updated favicon source
                   layout='centered',
                   initial_sidebar_state='collapsed')

# Display image using st.image
st.image('images/ConcertIDC_Logo_Stack.png', width=50, caption='')

# Page header with favicon icon
st.markdown(
    """
    <h1 style="display:flex; align-items:center;">
        LLAMA 2 Generate Blogs 
    </h1>
    """, 
    unsafe_allow_html=True
)

# Placeholder for topics and selected topic
if 'topics' not in st.session_state:
    st.session_state.topics = []

if 'selected_topic' not in st.session_state:
    st.session_state.selected_topic = None

# Input field for the blog topic
input_text = st.text_input("Enter the Blog Topic Keywords")

# Button to generate topics
generate_topics = st.button("Generate Topics")

# Generate and display topics
if generate_topics:
    with st.spinner('Generating topics...'):
        st.session_state.topics = generate_topics_from_llama(input_text)
        time.sleep(2)  # Simulate processing time

# Display generated topics in bullet format
if st.session_state.topics:
    # st.markdown("### Suggested Topics")
    # for topic in st.session_state.topics:
    #     st.markdown(f"- {topic}")
    
    # Selection for one of the topics
    selected_topic = st.selectbox('Select a Topic', st.session_state.topics)
    st.session_state.selected_topic = selected_topic

    # Optional input field for the number of words
    no_words = st.text_input('Number of Words (optional)', value='')

    # Dropdown selection for the blog style
    blog_style = st.selectbox('Writing the blog for', BLOG_STYLES, index=0)
    
    # Button to generate the blog content
    generate_blog = st.button("Generate Blog Content")

    # Display the generated blog response
    if generate_blog:
        with st.spinner('Generating blog content...'):
            if no_words == '':
                no_words = '500'  # Default to 500 words if not provided
            response = getLLamaResponse(st.session_state.selected_topic, no_words, blog_style)
            time.sleep(2)  # Simulate processing time
            st.write(response)