Update app.py

app.py

@@ -1,22 +1,43 @@
-# app.py
 import streamlit as st
 from transformers import pipeline
 
-st.title("Fake Job Posting Detector")
+# --- App title and description ---
+st.set_page_config(page_title="Fake Job / Lie Detector", layout="centered")
+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."
+)
 
-# Use a small model that fits in Hugging Face Space memory
-classifier = pipeline("zero-shot-classification", model="Groq/compound-mini")
+# --- Load zero-shot classification model ---
+@st.cache_resource
+def load_model():
+    return pipeline(
+        "zero-shot-classification",
+        model="typeform/distilbert-base-uncased-mnli"
+    )
 
-job_description = st.text_area("Enter the job description:")
+classifier = load_model()
 
+# --- Text input ---
+job_description = st.text_area("Enter the job description here:")
+
+# --- Button action ---
 if st.button("Check Job"):
-    if job_description.strip() == "":
-        st.warning("Please enter a job description.")
+    if not job_description.strip():
+        st.warning("⚠️ Please enter a job description first!")
     else:
-        labels = ["Legit", "Fake"]
-        result = classifier(job_description, candidate_labels=labels)
-        predicted_label = result['labels'][0]
-        confidence = result['scores'][0]
-        
-        st.write(f"Prediction: **{predicted_label}**")
-        st.write(f"Confidence: {confidence:.2f}")
+        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.")