Update app.py

app.py

@@ -1,44 +1,22 @@
+# app.py
 import streamlit as st
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
+from transformers import pipeline
 
-st.title("🚨 Fake Job Posting Detector")
-st.write(
-    "Enter a job description below to check if it is likely **Fake** or **Legit**. "
-    "This tool uses AI to help job seekers avoid scams."
-)
+st.title("Fake Job Posting Detector")
 
-# Load the model
-@st.cache_resource(show_spinner=True)
-def load_model():
-    model_id = "openai/gpt-oss-20b"
-    tokenizer = AutoTokenizer.from_pretrained(model_id)
-    model = AutoModelForCausalLM.from_pretrained(model_id)
-    return tokenizer, model
+# Use a small model that fits in Hugging Face Space memory
+classifier = pipeline("zero-shot-classification", model="Groq/compound-mini")
 
-tokenizer, model = load_model()
+job_description = st.text_area("Enter the job description:")
 
-# Input text
-job_description = st.text_area(
-    "Paste the job description here:",
-    "Example: Urgent hiring! Work from home, no experience needed, $5000/month!"
-)
-
-# Button to run prediction
-if st.button("Check Job Posting"):
+if st.button("Check Job"):
     if job_description.strip() == "":
-        st.warning("⚠️ Please enter a job description first.")
+        st.warning("Please enter a job description.")
     else:
-        # Prepare prompt for GPT-OSS
-        prompt = f"Classify the following job posting as Fake or Legit:\n\n{job_description}\n\nAnswer with only 'Fake' or 'Legit'."
-
-        inputs = tokenizer(prompt, return_tensors="pt")
-        with torch.no_grad():
-            outputs = model.generate(**inputs, max_new_tokens=20)
-        prediction = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-        # Display result with color
-        if "Fake" in prediction:
-            st.error(f"Prediction: Fake")
-        else:
-            st.success(f"Prediction: Legit")
+        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}")