Update app.py

app.py

@@ -1,43 +1,27 @@
 import streamlit as st
 from transformers import pipeline
 
-# --- App title and description ---
-st.set_page_config(page_title="Fake Job / Lie Detector", layout="centered")
-st.title("🔍 Fake Job / Lie Detector")
+st.title("Fake Job / Lie Detector")
+
 st.write(
-    "Enter a job description below and the AI will predict if it's likely genuine or fake."
+    "This app classifies a job description as potentially fake or real. "
+    "Runs entirely locally on CPU, no API key needed."
 )
 
-# --- Load zero-shot classification model ---
-@st.cache_resource
-def load_model():
-    return pipeline(
-        "zero-shot-classification",
-        model="typeform/distilbert-base-uncased-mnli"
-    )
-
-classifier = load_model()
+# Use a small, CPU-friendly model
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli", device=-1)
 
-# --- Text input ---
-job_description = st.text_area("Enter the job description here:")
+job_description = st.text_area("Enter the job description:")
 
-# --- Button action ---
 if st.button("Check Job"):
     if not job_description.strip():
-        st.warning("⚠️ Please enter a job description first!")
+        st.warning("Please enter a job description.")
     else:
-        candidate_labels = ["genuine", "fake"]
-        result = classifier(job_description, candidate_labels)
-
-        label = result['labels'][0]
-        confidence = round(result['scores'][0]*100, 2)
-
-        # --- Display results with color ---
-        if label == "genuine":
-            st.success(f"✅ Prediction: {label.upper()} ({confidence}%)")
-        else:
-            st.error(f"❌ Prediction: {label.upper()} ({confidence}%)")
-
-# --- Footer ---
-st.markdown("---")
-st.markdown("Built with ❤️ using Hugging Face Transformers and Streamlit.")
+        # Define the candidate labels
+        labels = ["real", "fake"]
+        result = classifier(job_description, candidate_labels=labels)
+        top_label = result["labels"][0]
+        score = result["scores"][0]
+
+        st.write(f"Prediction: **{top_label.upper()}**")
+        st.write(f"Confidence: **{score*100:.2f}%**")