app.py

import streamlit as st
from langchain.prompts import PromptTemplate
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
import os

# Authenticate with Hugging Face
# Add your Hugging Face token as an environment variable in Spaces or directly in the code


# Function to get the response back
def getLLMResponse(form_input, email_sender, email_recipient, email_style):
    # Load the tokenizer and model from the gated repository
    
    tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-2-7b-chat", use_auth_token=True)
    model = AutoModelForCausalLM.from_pretrained(
    "meta-llama/Llama-2-7b-chat", 
    trust_remote_code=True,
    use_auth_token=True
)

    # Create the pipeline
    generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

    # Template for building the PROMPT
    template = """
    Write an 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 the pipeline
    response = generator(
        prompt.format(
            email_topic=form_input,
            sender=email_sender,
            recipient=email_recipient,
            style=email_style,
        ),
        max_length=256,
        temperature=0.7,
    )

    # Extract and return the generated text
    return response[0]["generated_text"]


# Streamlit application setup
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:
    response = getLLMResponse(form_input, email_sender, email_recipient, email_style)
    st.write(response)