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import os
import streamlit as st
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
# Set HuggingFace API keys
hf_token = os.getenv("HF_Token")
os.environ["HUGGINGFACEHUB_API_TOKEN"] =hf_token
os.environ["HF_TOKEN"]=hf_token
llm_skeleton = HuggingFaceEndpoint(
repo_id="meta-llama/Llama-3.1-8B-Instruct",
provider="novita",
temperature=0.7,
max_new_tokens=150,
task="conversational"
)
llm = ChatHuggingFace(
llm=llm_skeleton,
repo_id="meta-llama/Llama-3.2-3B-Instruct",
provider="novita",
temperature=0.7,
max_new_tokens=150,
task="conversational"
)
# Helper function to extract text from uploaded file
def extract_text(file):
try:
loader = UnstructuredPDFLoader(file)
return loader.load()[0].page_content
except Exception as e:
st.error(f"Error reading file: {e}")
return ""
# Streamlit UI
st.set_page_config(page_title="Resume & JD Extractor", layout="centered")
st.title("📄 Resume & Job Description Extractor")
# Upload inputs
resume_file = st.file_uploader("Upload Resume (PDF)", type=["pdf"])
jd_file = st.file_uploader("Upload Job Description (PDF or TXT)", type=["pdf", "txt"])
jd_text = st.text_area("Or paste Job Description text here")
# Extract button
if st.button("🔍 Extract Data"):
if not resume_file and not (jd_file or jd_text):
st.warning("Please upload at least one file (Resume or JD) or paste JD text.")
# Extract Resume
if resume_file:
resume_text = extract_text(resume_file)
resume_prompt = (
"Extract the following from the resume:\n"
"1. Name\n2. Education\n3. Experience\n4. Skills\n5. Project Names and Results\n\n"
f"Resume:\n{resume_text}"
)
resume_data = llm.invoke(resume_prompt)
st.subheader("📌 Extracted Resume Data")
st.markdown(f"<div style='background-color:#f9f9f9;padding:10px;border-radius:8px;'>{resume_data}</div>", unsafe_allow_html=True)
# Extract JD
if jd_file or jd_text:
jd_text_extracted = extract_text(jd_file) if jd_file else jd_text
jd_prompt = (
"Extract the following from the job description:\n"
"1. Job ID\n2. Company Name\n3. Role\n4. Experience Required\n5. Skills Required\n"
"6. Education Required\n7. Location\n\n"
f"Job Description:\n{jd_text_extracted}"
)
jd_data = llm.invoke(jd_prompt)
st.subheader("📌 Extracted Job Description Data")
st.markdown(f"<div style='background-color:#f9f9f9;padding:10px;border-radius:8px;'>{jd_data}</div>", unsafe_allow_html=True)
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