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import streamlit as st
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import PyPDF2
from dotenv import load_dotenv
import os
# Load environment variables from .env
load_dotenv()
# API Key
access_token = os.getenv("API_KEY")
# Streamlit App Title
st.title("Job Description and CV-Based Email Generator")
st.write("""
This app uses Hugging Face's Gemma model to generate a professional email based on a pre-parsed CV and a job description.
Upload your CV once in the sidebar, and the system will reuse the parsed details for generating emails.
""")
# Sidebar for Settings and CV Upload
st.sidebar.title("Settings and CV Upload")
# File Upload for CV in Sidebar
uploaded_file = st.sidebar.file_uploader("Upload your CV (PDF format):", type=["pdf"])
if "parsed_cv" not in st.session_state:
st.session_state.parsed_cv = None
if "email_history" not in st.session_state:
st.session_state.email_history = []
if uploaded_file is not None:
try:
# Extract text from PDF
pdf_reader = PyPDF2.PdfReader(uploaded_file)
cv_text = "".join([page.extract_text() for page in pdf_reader.pages])
st.sidebar.success("CV uploaded and text extracted successfully!")
# Parse CV details and save to session state
def parse_cv(cv_text):
return f"""
Name: [Extracted Name]
Contact Information: [Extracted Contact Info]
Skills: [Extracted Skills]
Experience: [Extracted Experience]
Education: [Extracted Education]
Summary: {cv_text[:500]}... # Truncated summary of the CV
"""
st.session_state.parsed_cv = parse_cv(cv_text)
st.sidebar.success("CV parsed successfully!")
except Exception as e:
st.sidebar.error(f"Failed to extract text from CV: {e}")
if st.session_state.parsed_cv:
st.sidebar.write("### Parsed CV Details:")
st.sidebar.text(st.session_state.parsed_cv)
# Ensure Access Token is Provided
if access_token:
@st.cache_resource
def initialize_pipeline(access_token):
try:
tokenizer = AutoTokenizer.from_pretrained("google/gemma-2b-it", token=access_token)
model = AutoModelForCausalLM.from_pretrained(
"google/gemma-2b-it",
token=access_token,
)
return pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=2048,
temperature=0.7,
top_p=0.95
)
except Exception as e:
st.error(f"Failed to initialize the model: {str(e)}")
return None
text_gen_pipeline = initialize_pipeline(access_token)
# Input job description
job_description = st.text_area("Enter the job description:", "")
# Display generated email
if st.button("Generate Email"):
if st.session_state.parsed_cv and job_description.strip():
try:
# Improved prompt template
prompt = f"""Task: Write a professional job application email.
CV Summary:
{st.session_state.parsed_cv}
Job Description:
{job_description}
Instructions: Write a concise and professional email expressing interest in the position.
Highlight relevant experience and skills from the CV that match the job requirements.
Keep the tone professional and enthusiastic.
Email:
"""
# Generate email using the pipeline
if text_gen_pipeline:
response = text_gen_pipeline(
prompt,
clean_up_tokenization_spaces=True,
return_full_text=False
)[0]['generated_text']
# Save response in history
st.session_state.email_history.append({
"job_description": job_description,
"email": response
})
# Display response
st.subheader("Generated Email:")
st.write(response)
# Display conversation history
if st.session_state.email_history:
st.subheader("Previous Generations:")
for idx, entry in enumerate(st.session_state.email_history, 1):
st.write(f"### Email {idx}")
st.write(f"**Job Description:** {entry['job_description']}")
st.write(f"**Generated Email:** {entry['email']}")
else:
st.error("Text generation pipeline not properly initialized.")
except Exception as e:
st.error(f"Error generating email: {str(e)}")
else:
st.warning("Please upload your CV in the sidebar and enter a job description.")
else:
st.warning("Please enter your Hugging Face access token in the sidebar to use the app.")
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