app.py

import streamlit as st
from transformers import pipeline

# App title
st.set_page_config(page_title="Fake Job Detector", page_icon="🕵️‍♂️")
st.title("Fake Job / Lie Detector")
st.markdown("Enter a job description and check if it seems suspicious!")

# Load small NLI model for zero-shot classification (CPU-friendly)
@st.cache_resource
def load_model():
    return pipeline("zero-shot-classification", model="facebook/bart-large-mnli")

classifier = load_model()

# Input from user
job_description = st.text_area("Enter the job description:")

# Label options
labels = ["Legitimate", "Suspicious", "Fake", "Scam"]

# Button to check
if st.button("Check Job"):
    if not job_description.strip():
        st.warning("Please enter a job description!")
    else:
        # Run zero-shot classification
        results = classifier(job_description, candidate_labels=labels)
        st.subheader("Prediction:")
        # Show top label and scores
        top_label = results["labels"][0]
        score = results["scores"][0]
        st.success(f"Most likely: **{top_label}** ({score*100:.2f}%)")
        st.write("Full results:", results)