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from dotenv import load_dotenv
from langchain_google_genai import GoogleGenerativeAI
from langchain_core.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.runnables import RunnableParallel
load_dotenv()
st.set_page_config(page_title="Job Application Intelligence", page_icon="🧠", layout="centered")
st.title("🧠 Job Application Intelligence System")
st.caption("Resume vs Job Description – Parallel AI Brain")
# 1. LLM
llm = GoogleGenerativeAI(model="gemini-2.5-flash", temperature=0.2)
# 2. Prompts
match_prompt = PromptTemplate.from_template("""
You are an ATS system.
Given Resume and Job Description, calculate skill match percentage (0-100).
Return ONLY JSON:
{{
"match_percentage": number
}}
Resume:
{resume}
Job Description:
{jd}
""")
missing_prompt = PromptTemplate.from_template("""
You are a recruiter.
Find missing skills from resume compared to job description.
Return ONLY JSON:
{{
"missing_skills": [ "skill1", "skill2" ]
}}
Resume:
{resume}
Job Description:
{jd}
""")
improve_prompt = PromptTemplate.from_template("""
You are a career coach.
Suggest improvements to the resume for this job.
Return ONLY JSON:
{{
"improvement_suggestions": [ "point1", "point2" ]
}}
Resume:
{resume}
Job Description:
{jd}
""")
cover_prompt = PromptTemplate.from_template("""
You are an HR professional.
Write a short 3-line professional cover note for this job.
Return ONLY JSON:
{{
"cover_note": "3 lines cover note"
}}
Resume:
{resume}
Job Description:
{jd}
""")
parser = JsonOutputParser()
parallel_chain = RunnableParallel({
"match": match_prompt | llm | parser,
"missing": missing_prompt | llm | parser,
"improve": improve_prompt | llm | parser,
"cover": cover_prompt | llm | parser,
})
# UI Inputs
resume_text = st.text_area("📄 Paste your Resume", height=180)
jd_text = st.text_area("📌 Paste Job Description", height=180)
if st.button("Analyze Resume vs JD 🚀"):
if not resume_text.strip() or not jd_text.strip():
st.warning("Please provide both Resume and Job Description.")
else:
with st.spinner("Analyzing with Parallel AI Brain..."):
result = parallel_chain.invoke({
"resume": resume_text,
"jd": jd_text
})
st.subheader("📊 Results")
st.metric("Match Percentage", f"{result['match']['match_percentage']}%")
st.markdown("### ❌ Missing Skills")
st.write(result["missing"]["missing_skills"])
st.markdown("### ✍️ Improvement Suggestions")
for i, point in enumerate(result["improve"]["improvement_suggestions"], 1):
st.write(f"{i}. {point}")
st.markdown("### 📨 Custom Cover Note")
st.info(result["cover"]["cover_note"])
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