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app.py

@@ -126,45 +126,41 @@ print("All models ready.")
 # ---------------------------------------------------------------------------
 # Prediction helper
 # ---------------------------------------------------------------------------
-LABEL_MAP = {0: "✅ Ham (not spam)", 1: "🚨 SPAM"}
-COLOUR_MAP = {0: "green", 1: "red"}
+LABEL_MAP  = {0: "✅ Ham (not spam)", 1: "🚨 SPAM"}
+BG_MAP     = {0: "#1a3a1a", 1: "#3a1a1a"}
+BORDER_MAP = {0: "#2ecc71", 1: "#e74c3c"}
+COLOR_MAP  = {0: "#2ecc71", 1: "#e74c3c"}
 
 def predict_sms(text: str):
     if not text or not text.strip():
-        return (
-            "—", "—", "—", "—",
-            "Please enter a message above.",
-        )
-
-    results = []
-    verdict_html_parts = []
+        return "<p style='color:#888; padding:12px;'>⬆️ Enter a message above and click <b>Classify</b>.</p>"
 
+    cards = []
     for model_name, model in MODELS.items():
-        pred = int(model.predict([text])[0])
-        label = LABEL_MAP[pred]
-        colour = COLOUR_MAP[pred]
+        pred   = int(model.predict([text])[0])
+        label  = LABEL_MAP[pred]
+        bg     = BG_MAP[pred]
+        border = BORDER_MAP[pred]
+        colour = COLOR_MAP[pred]
         contrib = CONTRIBUTORS[model_name]
 
-        verdict_html_parts.append(
-            f"<div style='border:1px solid #ddd; border-radius:8px; padding:12px; margin:6px 0;'>"
-            f"<b>{contrib['emoji']} {model_name}</b> &nbsp;"
-            f"<span style='color:{colour}; font-weight:bold;'>{label}</span><br>"
-            f"<small style='color:#888;'>by {contrib['name']} ({contrib['id']})</small>"
+        cards.append(
+            f"<div style='"
+            f"background:{bg}; border:2px solid {border}; border-radius:12px;"
+            f"padding:16px 20px; flex:1; min-width:180px;'>"
+            f"<div style='font-size:1.6em; margin-bottom:4px;'>{contrib['emoji']}</div>"
+            f"<div style='font-weight:700; font-size:1.05em; color:#fff;'>{model_name}</div>"
+            f"<div style='color:{colour}; font-size:1.25em; font-weight:800; margin:8px 0;'>{label}</div>"
+            f"<div style='color:#aaa; font-size:0.8em;'>{contrib['name']}<br>{contrib['id']}</div>"
             f"</div>"
         )
 
-        results.append(label)
-
-    summary_html = (
-        "<h3 style='margin-bottom:8px;'>Predictions from all 4 models</h3>"
-        + "".join(verdict_html_parts)
-    )
-
-    svm_result, lr_result, nb_result, xgb_result = (
-        results[0], results[2], results[1], results[3]
+    grid = (
+        "<div style='display:flex; gap:12px; flex-wrap:wrap; margin-top:8px;'>"
+        + "".join(cards)
+        + "</div>"
     )
-
-    return svm_result, xgb_result, lr_result, nb_result, summary_html
+    return grid
 
 
 # ---------------------------------------------------------------------------
@@ -276,13 +272,9 @@ with gr.Blocks(title="SMS Spam Detector") as demo:
 
             classify_btn = gr.Button("Classify ▶", variant="primary", size="lg")
 
-            with gr.Row():
-                out_svm = gr.Label(label="⚡ Linear SVM\n(Sanjivan TP070073)")
-                out_xgb = gr.Label(label="🌲 XGBoost\n(Mohamud Farah TP076875)")
-                out_lr  = gr.Label(label="📊 Logistic Regression\n(Farouk TP075438)")
-                out_nb  = gr.Label(label="🔢 Multinomial NB\n(Devara TP073570)")
-
-            summary_out = gr.HTML()
+            results_out = gr.HTML(
+                value="<p style='color:#888; padding:12px;'>⬆️ Enter a message above and click <b>Classify</b>.</p>"
+            )
 
             with gr.Accordion("🚨 Spam Examples — click any to load", open=False):
                 gr.Examples(
@@ -303,13 +295,13 @@ with gr.Blocks(title="SMS Spam Detector") as demo:
             classify_btn.click(
                 fn=predict_sms,
                 inputs=sms_input,
-                outputs=[out_svm, out_xgb, out_lr, out_nb, summary_out],
+                outputs=results_out,
             )
 
             sms_input.submit(
                 fn=predict_sms,
                 inputs=sms_input,
-                outputs=[out_svm, out_xgb, out_lr, out_nb, summary_out],
+                outputs=results_out,
             )
 
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