import streamlit as st from huggingface_hub import InferenceClient from transformers import AutoTokenizer, AutoModelForCausalLM import torch import os from PyPDF2 import PdfReader import docx import re import smtplib from email.mime.multipart import MIMEMultipart from email.mime.text import MIMEText from email.mime.base import MIMEBase from email import encoders from typing import Dict def extract_cv_text(file): """Extract text from PDF or DOCX CV files.""" if file is None: return "No CV uploaded" file_ext = os.path.splitext(file.name)[1].lower() text = "" try: if file_ext == '.pdf': reader = PdfReader(file) for page in reader.pages: text += page.extract_text() elif file_ext == '.docx': doc = docx.Document(file) for paragraph in doc.paragraphs: text += paragraph.text + '\n' else: return "Unsupported file format. Please upload PDF or DOCX files." return text # Return the full text instead of parsed sections except Exception as e: return f"Error processing file: {str(e)}" # Replace 'your_huggingface_token' with your actual Hugging Face access token access_token = os.getenv('API_KEY') # Initialize the inference client (if needed for other API-based tasks) client = InferenceClient(token=access_token) def create_email_prompt(job_description: str, cv_text: str) -> str: """Create a detailed prompt for email generation.""" return f"""Job Description: {job_description} Your CV Details: {cv_text} Instructions: Write a professional job application email following these guidelines: 1. Start with a proper greeting 2. First paragraph: Express interest in the position and mention how you found it 3. Second paragraph: Highlight 2-3 most relevant experiences from your CV that match the job requirements 4. Third paragraph: Mention specific skills that align with the role 5. Closing paragraph: Express enthusiasm for an interview. Use the exact contact information provided in the CV - do not use placeholders like [phone] or [email] 6. End with a professional closing Important: Use the exact contact details and information from the CV. Do not generate or make up any placeholder information. Keep the tone professional, confident, and enthusiastic. Be concise but impactful. Email:""" def conversation_predict(input_text: str, cv_text: str): """Generate a response using the model with streaming output.""" prompt = create_email_prompt(input_text, cv_text) # Use the streaming API try: for response in client.text_generation( model="google/gemma-2b-it", prompt=prompt, max_new_tokens=512, temperature=0.7, top_p=0.95, stream=True ): # The streaming response returns text directly yield response except Exception as e: st.error(f"Error generating response: {str(e)}") yield "" def respond( message: str, history: list[tuple[str, str]], system_message: str, cv_file, max_tokens: int, temperature: float, top_p: float, ): """Generate a response for a multi-turn chat conversation.""" # Extract CV text and update system message cv_text = extract_cv_text(cv_file) if cv_file else "No CV provided" updated_system_message = f"""Task: Write a professional job application email. CV Summary: {cv_text} {system_message}""" messages = [{"role": "system", "content": updated_system_message}] for user_input, assistant_reply in history: if user_input: messages.append({"role": "user", "content": user_input}) if assistant_reply: messages.append({"role": "assistant", "content": assistant_reply}) messages.append({"role": "user", "content": message}) response = "" for message_chunk in client.chat_completion( messages=messages, max_tokens=max_tokens, stream=True, temperature=temperature, top_p=top_p, ): token = message_chunk["choices"][0]["delta"].get("content", "") response += token yield response # Function to send the email with attachment def send_email(sender_email: str, receiver_email: str, subject: str, body: str, attachment_path: str): """Send email with CV attachment.""" try: msg = MIMEMultipart() msg['From'] = sender_email msg['To'] = receiver_email msg['Subject'] = subject msg.attach(MIMEText(body, 'plain')) # Attach the CV file if attachment_path: attachment = open(attachment_path, "rb") part = MIMEBase('application', 'octet-stream') part.set_payload(attachment.read()) encoders.encode_base64(part) part.add_header('Content-Disposition', f'attachment; filename={os.path.basename(attachment_path)}') msg.attach(part) # Set up the server and send the email server = smtplib.SMTP('smtp.gmail.com', 587) server.starttls() server.login(sender_email, os.getenv('EMAIL_PASSWORD')) # Replace with your email credentials text = msg.as_string() server.sendmail(sender_email, receiver_email, text) server.quit() st.success("Email sent successfully!") except Exception as e: st.error(f"Error sending email: {str(e)}") # Streamlit UI section st.title("AI Job Application Email Generator") def update_ui(message, cv_file, cv_text): """Handle the UI updates for email generation.""" # Create placeholder for the generated email email_placeholder = st.empty() email_text = "" # Initialize email_text before use # Generate button if st.button("Generate Email", key="generate_button"): if message and cv_file and isinstance(cv_text, str) and not cv_text.startswith("Error"): email_text = "" # Stream the response try: with st.spinner('Generating your application email...'): for chunk in conversation_predict(message, cv_text): if chunk: email_text += chunk # Update the text area with each chunk, using timestamp in key email_placeholder.text_area( "Generated Email", value=email_text, height=400 ) st.success('Email generated successfully!') except Exception as e: st.error(f"Error during email generation: {str(e)}") else: st.warning("Please upload a CV and enter a job description.") # Email input fields st.markdown("### Sender & Receiver Information") sender_email = st.text_input("Sender's Email Address") receiver_email = st.text_input("Receiver's Email Address") # Email subject subject = st.text_input("Subject", value="Job Application for [Position Name]") # Option to edit the generated email email_body = st.text_area("Edit the Generated Email (if needed)", value=email_text, height=400) # Send email button if st.button("Send Email"): if sender_email and receiver_email and email_body: send_email(sender_email, receiver_email, subject, email_body, cv_file.name) # Add tabs for different sections tab1, tab2 = st.tabs(["Generate Email", "View CV Details"]) with tab1: # CV file upload cv_file = st.file_uploader("Upload CV (PDF or DOCX)", type=["pdf", "docx"]) if cv_file: cv_text = extract_cv_text(cv_file) if isinstance(cv_text, str) and not cv_text.startswith("Error"): st.success("CV uploaded successfully!") else: st.error(cv_text) cv_text = None else: cv_text = None # Job description input st.markdown("### Job Description") message = st.text_area("Paste the job description here:", height=200) # Call the updated UI function with parameters update_ui(message, cv_file, cv_text) with tab2: if cv_file and isinstance(cv_text, str) and not cv_text.startswith("Error"): st.markdown("### CV Content") st.text_area("Full CV Text", value=cv_text, height=400) else: st.info("Upload a CV to view content")