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import streamlit as st
from langchain_community.document_loaders import WebBaseLoader
from chains import Chain
# from portfolio import Portfolio
from utils import clean_text, extract_text_from_pdf
def create_streamlit_app(llm, clean_text):
st.title("📧 Welcome to Cold E-Mail Generator")
# PDF upload section
uploaded_file = st.file_uploader("Upload your resume as PDF", type=["pdf"])
pdf_text = extract_text_from_pdf(uploaded_file)
# if pdf_text:
# st.text_area("Extracted Text", value=pdf_text, height=300)
url_input = st.text_input("Enter the URL of Job Posting:", value="https://careers.myntra.com/job-detail/?id=7431200002")
submit_button = st.button("Generate E-mail")
if submit_button:
try:
loader = WebBaseLoader([url_input])
data = clean_text(loader.load().pop().page_content) # cleans any unnecessary garbage text
jobs = llm.extract_jobs(data) # create json objects for the job
for job in jobs: # this is for if one web page has multiple jobs
# skills = job.get('skills', [])
summarized_text = llm.summarize_pdf(pdf_text)
# st.text_area(summarized_text)
email = llm.write_mail(job, summarized_text) # write the email
# st.code(email, language='markdown')
st.text_area("Email is as follows", value=email, height=500)
# st.code('hello')
except Exception as e:
st.error(f"An Error Occurred: {e}")
if __name__ == "__main__":
chain = Chain()
# portfolio = Portfolio()
st.set_page_config(layout="wide", page_title="Cold Email Generator", page_icon="📧")
create_streamlit_app(chain, clean_text)
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