Update app.py

app.py

@@ -1,62 +1,37 @@
-import streamlit as st
+import gradio as gr
 from transformers import pipeline
 from PIL import Image
 import pytesseract
 from pdf2image import convert_from_bytes
 
-# -------------------
-# Load Hugging Face text classifier
-# -------------------
-@st.cache_resource
-def load_classifier():
-    classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
-    return classifier
+# Load classifier
+classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
 
-classifier = load_classifier()
-
-# -------------------
-# App UI
-# -------------------
-st.title("Fake Job Detector")
-st.write("Paste job text or upload a screenshot/PDF to check if the job posting is Legitimate or Fake.")
-
-# Job text input
-job_text = st.text_area("Paste job description here:")
-
-# File upload (image or PDF)
-uploaded_file = st.file_uploader("Or upload a screenshot / PDF", type=["png","jpg","jpeg","pdf"])
-
-extracted_text = ""
-
-# Extract text from file if uploaded
-if uploaded_file:
-    if uploaded_file.type == "application/pdf":
-        images = convert_from_bytes(uploaded_file.read())
-        for img in images:
-            extracted_text += pytesseract.image_to_string(img) + "\n"
-    else:
-        img = Image.open(uploaded_file)
-        extracted_text = pytesseract.image_to_string(img)
-
-# Combine pasted text + extracted text
-full_text = job_text + "\n" + extracted_text
-
-if st.button("Detect"):
-    if full_text.strip() == "":
-        st.warning("Please paste job text or upload a file!")
-    else:
-        result = classifier(full_text)
-        label = result[0]['label']
-        score = result[0]['score']
-
-        # Map labels to Fake/Suspicious/Legit (simple demo)
-        # Here using SST-2 labels for demo; in real app, fine-tune model
-        if label == "NEGATIVE":
-            final_label = "Suspicious / Fake"
+def detect_job(text, file):
+    extracted_text = ""
+    if file:
+        if file.name.endswith(".pdf"):
+            images = convert_from_bytes(file.read())
+            for img in images:
+                extracted_text += pytesseract.image_to_string(img) + "\n"
         else:
-            final_label = "Legitimate"
-
-        st.success(f"Prediction: **{final_label}**")
-        st.info(f"Confidence: {score:.2f}")
-        st.subheader("Extracted Job Text:")
-        st.text_area("Text Extracted", full_text, height=200)
+            img = Image.open(file)
+            extracted_text = pytesseract.image_to_string(img)
+    full_text = text + "\n" + extracted_text
+    if full_text.strip() == "":
+        return "No text provided!"
+    result = classifier(full_text)
+    label = "Legitimate" if result[0]['label'] == "POSITIVE" else "Suspicious / Fake"
+    score = result[0]['score']
+    return f"Prediction: {label} (Confidence: {score:.2f})"
+
+# Gradio UI
+iface = gr.Interface(
+    fn=detect_job,
+    inputs=[gr.Textbox(lines=10, placeholder="Paste job description here..."), 
+            gr.File(type=["pdf","png","jpg","jpeg"])],
+    outputs="text",
+    title="Fake Job Detector"
+)
+
+iface.launch()