Update app.py

app.py

@@ -1,126 +1,55 @@
-import gradio as gr
+ import gradio as gr
 from transformers import pipeline
 
-# Load model
+# Load a better model for Bangla & English fake news detection
 classifier = pipeline(
     "text-classification",
-    model="mrm8488/bert-tiny-finetuned-fake-news-detection"
+    model="armansakif/bengali-fake-news",
+    tokenizer="armansakif/bengali-fake-news"
 )
 
 def predict_news(text):
     if text.strip() == "":
-        return "<span style='color:red;'>Please enter news text</span>", 0
+        return "<span style='color:red;'>⚠️ Please enter news text</span>", 0
 
-    result = classifier(text)[0]
-    label = result["label"]
-    confidence = round(result["score"] * 100, 2)
-
-    # Correct label mapping
-    if label == "LABEL_0":
-        display_label = "Fake"
-        color = "red"
-    else:
-        display_label = "Real"
-        color = "green"
-
-    html_output = f"""
-    <div style="
-        font-size:28px;
-        font-weight:bold;
-        color:{color};
-        text-align:center;
-        padding:20px;
-    ">
-        {display_label} {confidence}%
-    </div>
-    """
-
-    return html_output, confidence
-
-
-# ================= UI LAYOUT =================
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-
-    gr.Markdown(
-        """
-        # 📰 Fake News Detection System  
-        ### AI-based system to detect Fake or Real news (English & Bangla)
-        """
-    )
-
-    with gr.Row():
-        with gr.Column(scale=2):
-            news_input = gr.Textbox(
-                label="Enter News Text",
-                placeholder="Paste English or Bangla news here...",
-                lines=10
-            )
-            submit_btn = gr.Button("Submit", variant="primary")
-
-        with gr.Column(scale=1):
-            prediction_output = gr.HTML(label="Prediction")
-            confidence_bar = gr.Slider(
-                minimum=0,
-                maximum=100,
-                label="Confidence Percentage",
-                interactive=False
-            )
-
-    submit_btn.click(
-        fn=predict_news,
-        inputs=news_input,
-        outputs=[prediction_output, confidence_bar]
-    )
-
-demo.launch()
- 
-import gradio as gr
-from transformers import pipeline
+    # Run classification
+    try:
+        result = classifier(text)[0]
+    except Exception as e:
+        return f"<span style='color:red;'>Error: {str(e)}</span>", 0
 
-# Load model
-classifier = pipeline(
-    "text-classification",
-    model="mrm8488/bert-tiny-finetuned-fake-news-detection"
-)
-
-def predict_news(text):
-    if text.strip() == "":
-        return "<span style='color:red;'>Please enter news text</span>", 0
-
-    result = classifier(text)[0]
     label = result["label"]
     confidence = round(result["score"] * 100, 2)
 
-    # Correct label mapping
-    if label == "LABEL_0":
-        display_label = "Fake"
+    # Map labels to understandable text
+    if label.upper() in ["LABEL_0", "fake", "Fake"]:
+        display_label = "🟥 Fake News"
         color = "red"
     else:
-        display_label = "Real"
+        display_label = "🟩 Real News"
         color = "green"
 
     html_output = f"""
     <div style="
-        font-size:28px;
-        font-weight:bold;
-        color:{color};
-        text-align:center;
-        padding:20px;
+        font-size: 28px;
+        font-weight: bold;
+        color: {color};
+        text-align: center;
+        padding: 20px;
     ">
-        {display_label} {confidence}%
+        {display_label} — {confidence}%
     </div>
     """
 
     return html_output, confidence
 
-
 # ================= UI LAYOUT =================
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
     gr.Markdown(
         """
         # 📰 Fake News Detection System  
-        ### AI-based system to detect Fake or Real news (English & Bangla)
+        **AI‑powered fake vs real news classifier (English & Bangla)**
         """
     )
 
@@ -150,3 +79,4 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
 
 demo.launch()
 
+