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import streamlit as st
from langchain.prompts import PromptTemplate
from langchain.llms import CTransformers
#Function to get the response back
def getLLMResponse(form_input,email_sender,email_recipient,email_style):
#llm = OpenAI(temperature=.9, model="text-davinci-003")
# Wrapper for Llama-2-7B-Chat, Running Llama 2 on CPU
#Quantization is reducing model precision by converting weights from 16-bit floats to 8-bit integers,
#enabling efficient deployment on resource-limited devices, reducing model size, and maintaining performance.
#C Transformers offers support for various open-source models,
#among them popular ones like Llama, GPT4All-J, MPT, and Falcon.
#C Transformers is the Python library that provides bindings for transformer models implemented in C/C++ using the GGML library
llm = CTransformers(model='models/llama-2-7b-chat.ggmlv3.q8_0.bin', #https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main
model_type='llama',
config={'max_new_tokens': 256,
'temperature': 0.01})
#Template for building the PROMPT
template = """
Write a email with {style} style and includes topic :{email_topic}.\n\nSender: {sender}\nRecipient: {recipient}
\n\nEmail Text:
"""
#Creating the final PROMPT
prompt = PromptTemplate(
input_variables=["style","email_topic","sender","recipient"],
template=template,)
#Generating the response using LLM
response=llm(prompt.format(email_topic=form_input,sender=email_sender,recipient=email_recipient,style=email_style))
print(response)
return response
st.set_page_config(page_title="Generate Emails",
page_icon='📧',
layout='centered',
initial_sidebar_state='collapsed')
st.header("Generate Emails 📧")
form_input = st.text_area('Enter the email topic', height=275)
#Creating columns for the UI - To receive inputs from user
col1, col2, col3 = st.columns([10, 10, 5])
with col1:
email_sender = st.text_input('Sender Name')
with col2:
email_recipient = st.text_input('Recipient Name')
with col3:
email_style = st.selectbox('Writing Style',
('Formal', 'Appreciating', 'Not Satisfied', 'Neutral'),
index=0)
submit = st.button("Generate")
#When 'Generate' button is clicked, execute the below code
if submit:
st.write(getLLMResponse(form_input,email_sender,email_recipient,email_style)) |