Update app.py

app.py

@@ -1,25 +1,24 @@
 import gradio as gr
 from transformers import pipeline
 
-# Use a valid public model
-classifier = pipeline("text-classification", model="michiyasunaga/BERT-fake-news-detection")
+# ✅ Use a working fake news detection model
+classifier = pipeline("text-classification", model="mrm8488/bert-tiny-finetuned-fake-news-detection")
 
+# 💡 Function to classify news
 def detect_fake_news(text):
     result = classifier(text)[0]
-    label = result['label']
-    score = result['score']
-    
-    explanation = (
-        f"The model predicts this news is **{label}** "
-        f"with a confidence of **{score:.2f}**.\n\n"
-        "🧠 This is based on BERT fine-tuned on a fake news dataset."
-    )
-    return explanation
+    label = result["label"]
+    score = result["score"]
+    explanation = "🟢 This looks like real news." if label == "REAL" else "🔴 This might be fake news."
+    return f"Prediction: {label}\nConfidence: {score:.2f}\n{explanation}"
 
-demo = gr.Interface(fn=detect_fake_news,
-                    inputs=gr.Textbox(lines=10, placeholder="Paste your news article here..."),
-                    outputs="markdown",
-                    title="🕵️ Fake News Detector",
-                    description="An NLP app that predicts whether a news article is fake or real using BERT.")
+# 🎛️ Gradio UI
+iface = gr.Interface(
+    fn=detect_fake_news,
+    inputs=gr.Textbox(lines=10, placeholder="Paste your news article here..."),
+    outputs="text",
+    title="📰 Fake News Detector",
+    description="Detect whether a news article is real or fake using a fine-tuned BERT model."
+)
 
-demo.launch()
+iface.launch()