app.py

import gradio as gr
from transformers import AutoTokenizer, AutoModelForCausalLM
from huggingface_hub import login
import os
from dotenv import load_dotenv
import logging
import sys  # Ensure sys is imported

# Load environment variables
load_dotenv()
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s', stream=sys.stdout)

# Login to Hugging Face using the token
hf_token = os.getenv('HUGGING_FACE_TOKEN')
if hf_token:
    #login(token=hf_token)
    os.environ["HUGGINGFACEHUB_API_TOKEN"] = hf_token
else:
    raise ValueError("HUGGING_FACE_TOKEN environment variable not set.")

# Load the Llama-3.1-8B model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")

# Function to generate a formatted email
def generate_email(recipient_name, recipient_email, industry, recipient_role, details):
    prompt = (
        f"Write a cold outreach email for a {recipient_role} "
        f"working in the {industry} industry. Use the following details: {details}. "
        "The email should be professional and engaging, without any additional instructions or templates."
    )
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(**inputs, max_length=300, num_return_sequences=1, temperature=0.7)
    email_body = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Extract only the relevant email content
    email_lines = email_body.split('\n')
    relevant_lines = []
    for line in email_lines:
        if line.strip().lower().startswith(('dear', 'hello', 'hi')):
            relevant_lines = [line]
        elif relevant_lines:
            if line.strip().lower().startswith(('sincerely', 'best regards', 'regards', 'thank you')):
                break
            relevant_lines.append(line)

    cleaned_email_body = '\n'.join(relevant_lines).strip()

    # Format the email
    formatted_email = f"""\
To: {recipient_name} <{recipient_email}>
Subject: Collaboration Opportunity

{cleaned_email_body}

Best regards,
Jane Smith
Android Developer
Albertsons
[Your Contact Information]
"""
    return formatted_email

# Create Gradio interface
iface = gr.Interface(
    fn=generate_email,
    inputs=[
        gr.Textbox(lines=1, label="Recipient Name"),
        gr.Textbox(lines=1, label="Recipient Email"),
        gr.Textbox(lines=1, label="Industry (e.g., Technology, Healthcare)"),
        gr.Textbox(lines=1, label="Recipient Role (e.g., Manager, Director)"),
        gr.Textbox(lines=5, label="Personal/Company Details (e.g., name, product)"),
    ],
    outputs="text",
    title="EmailGenie: AI-Powered Email Generator",
    description="Automate the creation of personalized emails to increase engagement and conversion rates. Enter details to generate tailored emails."
)

# Launch the app
if __name__ == '__main__':
    iface.launch()