import streamlit as st
from langchain.prompts import PromptTemplate
#from langchain.llms import CTransformers
from langchain import HuggingFaceHub

# Function to get response from Llama 2 model

def getLlamaresponse(input_text, no_words, blog_style):
    #llm = CTransformers(model = 'models\llama-2-7b-chat.Q8_0.gguf', model_type = 'llama',config  ={'max_new_tokens': 256, 'temperature':0.01})
    llm = HuggingFaceHub(
        repo_id='meta-llama/Llama-2-7b-hf',
        model_kwargs={'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 Response from llama2
    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")

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 Scientists','Common People'), index = 0)

submit = st.button('Generate')

if submit:
    st.write(getLlamaresponse(input_text, no_words, blog_style))