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import os
import streamlit as st
from langchain_community.document_loaders import UnstructuredPDFLoader
from langchain_huggingface import HuggingFaceEndpoint, ChatHuggingFace
import tempfile
os.environ["HF_TOKEN"]=os.getenv('HF_Token')
os.environ["HUGGINGFACEHUB_API_KEY"]=os.getenv('HF_Token')
llm_skeleton = HuggingFaceEndpoint(
repo_id="deepseek-ai/DeepSeek-R1",
provider="nebius",
temperature=0.7,
max_new_tokens=150,
task="conversational"
)
llm = ChatHuggingFace(
llm=llm_skeleton,
repo_id="deepseek-ai/DeepSeek-R1",
provider="nebius",
temperature=0.7,
max_new_tokens=150,
task="conversational"
)
def extract_text_from_pdf(uploaded_file):
try:
with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_path = tmp_file.name
loader = UnstructuredPDFLoader(tmp_path)
return loader.load()[0].page_content
except Exception as e:
st.error(f"⚠️ Error reading PDF file: {e}")
return ""
def extract_text_from_txt(uploaded_file):
try:
return uploaded_file.read().decode("utf-8")
except Exception as e:
st.error(f"⚠️ Error reading text file: {e}")
return ""
st.set_page_config(page_title="SmartHire AI – Resume & JD Analyzer", layout="centered")
st.markdown("<h1 style='text-align:center;'>🤖 SmartHire AI</h1>", unsafe_allow_html=True)
st.markdown("<h4 style='text-align:center;color:gray;'>AI-powered Resume & Job Description Analyzer</h4><br>", unsafe_allow_html=True)
with st.container():
st.markdown("### 📂 Upload Center")
col1, col2 = st.columns(2)
with col1:
resume_file = st.file_uploader("Upload Candidate Resume (PDF)", type=["pdf"])
with col2:
jd_file = st.file_uploader("Upload Job Description (PDF or TXT)", type=["pdf", "txt"])
jd_text = st.text_area("📋 Or paste the Job Description text below", height=150)
if st.button("🚀 Analyze Now"):
if not resume_file and not (jd_file or jd_text):
st.warning("⚠️ Please upload at least one resume or job description.")
else:
if resume_file:
resume_text = extract_text_from_pdf(resume_file)
if resume_text:
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.markdown("---")
st.subheader("📘 Candidate Summary")
st.markdown(f"""
<div style='background-color:#eef6fb;padding:15px;border-left:5px solid #0099cc;border-radius:6px'>
{resume_data}
</div>
""", unsafe_allow_html=True)
jd_text_extracted = ""
if jd_file:
if jd_file.type == "application/pdf":
jd_text_extracted = extract_text_from_pdf(jd_file)
elif jd_file.type == "text/plain":
jd_text_extracted = extract_text_from_txt(jd_file)
elif jd_text:
jd_text_extracted = jd_text
if jd_text_extracted:
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.markdown("---")
st.subheader("📄 Job Role Breakdown")
st.markdown(f"""
<div style='background-color:#fff7e6;padding:15px;border-left:5px solid #ffaa00;border-radius:6px'>
{jd_data}
</div>
""", unsafe_allow_html=True) |