Upload app.py

app.py

@@ -0,0 +1,157 @@
+import gradio as gr
+import joblib
+import numpy as np
+import re
+
+# ----------------------------
+# Load the trained model
+# ----------------------------
+model = joblib.load("spam_model.joblib")
+
+# ----------------------------
+# Text normalization (optional but helps)
+# ----------------------------
+_url = re.compile(r'https?://\S+|www\.\S+')
+_email = re.compile(r'\b[\w\.-]+@[\w\.-]+\.\w+\b')
+_num = re.compile(r'\b\d[\d,.\-:/]*\b')
+
+def normalize_text(t: str) -> str:
+    t = t.strip().lower()
+    t = _url.sub(' __url__ ', t)
+    t = _email.sub(' __email__ ', t)
+    t = _num.sub(' __number__ ', t)
+    return t
+
+# ----------------------------
+# Prediction function with confidence
+# ----------------------------
+def predict_with_confidence(text: str):
+    text_norm = normalize_text(text)
+    _input = [text_norm if text_norm else text]
+
+    pred = int(model.predict(_input)[0])
+
+    if hasattr(model, "predict_proba"):
+        proba = float(model.predict_proba(_input)[0][1])  # spam prob
+    else:
+        score = float(model.decision_function(_input)[0])
+        proba = 1 / (1 + np.exp(-score))  # sigmoid
+
+    label = "SPAM 🚨" if pred == 1 else "HAM ✅"
+    return label, proba
+
+# ----------------------------
+# Custom CSS
+# ----------------------------
+CUSTOM_CSS = """
+:root {
+  --brand: #0ea5e9;
+  --brand-2: #0284c7;
+  --success: #22c55e;
+  --danger: #ef4444;
+  --card-bg: #0b1220;
+  --ink: #e5e7eb;
+}
+.gradio-container {max-width: 2100px !important;}
+#app-card {
+  background: linear-gradient(135deg, #0b1220 0%, #0f172a 100%);
+  color: var(--ink);
+  border-radius: 18px;
+  padding: 28px;
+  box-shadow: 0 10px 30px rgba(0,0,0,0.35);
+  border: 1px solid rgba(255,255,255,0.08);
+}
+#title {font-size: 28px;font-weight: 800;letter-spacing: 0.2px;}
+.subtitle {opacity: 0.85;margin-top: 2px;font-size: 14px;}
+.badge {
+  display: inline-block;padding: 4px 10px;border-radius: 999px;
+  background: linear-gradient(135deg, var(--brand) 0%, var(--brand-2) 100%);
+  color: white;font-weight: 600;font-size: 12px;
+}
+#predict-btn > button {
+  background: linear-gradient(135deg, var(--brand) 0%, var(--brand-2) 100%) !important;
+  border: none !important;color: white !important;font-weight: 700 !important;
+  border-radius: 10px !important;
+}
+#clear-btn > button {
+  background: transparent !important;border: 1px solid rgba(255,255,255,0.15) !important;
+  color: var(--ink) !important;font-weight: 600 !important;border-radius: 10px !important;
+}
+.result-card {
+  border-radius: 14px;padding: 14px 16px;border: 1px solid rgba(255,255,255,0.08);
+  display: flex;align-items: center;gap: 10px;
+}
+.result-label {font-size: 18px;font-weight: 800;}
+.confidence-wrap {display: flex;align-items: center;gap: 10px;}
+.confidence-bar {
+  height: 10px;border-radius: 999px;flex: 1;
+  background: linear-gradient(90deg, var(--danger), var(--success));
+  position: relative;overflow: hidden;
+}
+.confidence-fill {
+  position: absolute;top: 0;left: 0;bottom: 0;
+  background: rgba(255,255,255,0.9);
+  mix-blend-mode: overlay;
+}
+.footer {opacity: 0.7;font-size: 12px;margin-top: 8px;}
+"""
+
+# ----------------------------
+# UI Prediction wrapper
+# ----------------------------
+def ui_predict(msg):
+    label, proba = predict_with_confidence(msg)
+    pct = int(round(proba * 100))
+    color = "var(--danger)" if "SPAM" in label else "var(--success)"
+    styled_label = f"<span style='color:{color}'>{label}</span>"
+    html = f"""
+    <div class="result-card">
+      <div class="result-label">{styled_label}</div>
+      <div class="confidence-wrap" style="flex:1">
+        <div style="min-width:120px; text-align:right; font-weight:700;">{pct}%</div>
+        <div class="confidence-bar">
+          <div class="confidence-fill" style="width:{pct}%;"></div>
+        </div>
+      </div>
+    </div>
+    """
+    return html
+
+# ----------------------------
+# Gradio App
+# ----------------------------
+with gr.Blocks(css=CUSTOM_CSS, theme=gr.themes.Soft()) as demo:
+    with gr.Column(elem_id="app-card"):
+        gr.HTML("""
+          <div id="title">📩 SMS Spam Classifier <span class="badge">TF-IDF + Logistic Regression</span></div>
+          <div class="subtitle">Type an SMS below and get an instant prediction with confidence.</div>
+        """)
+        with gr.Row():
+            msg = gr.Textbox(
+                label="Your message",
+                placeholder="e.g., Congratulations! You have won a FREE vacation. Text WIN to 90909 now!",
+                lines=5
+            )
+        with gr.Row():
+            predict_btn = gr.Button("Predict", elem_id="predict-btn")
+            clear_btn = gr.Button("Clear", elem_id="clear-btn")
+        result = gr.HTML(label="Result")
+
+        with gr.Accordion("Try examples", open=False):
+            gr.Examples(
+                examples=[
+                    ["Hey, are we still meeting at 7 tonight?"],
+                    ["Don’t forget to bring your homework tomorrow."],
+                    ["URGENT! Claim your ��1000 cash prize by calling 0800-123-456."],
+                    ["Free entry in 2 a weekly comp for a chance to win an iPhone. Text WIN to 88888."]
+                ],
+                inputs=[msg]
+            )
+        gr.Markdown('<div class="footer">Tip: spam words like "free", "win", "claim", "prize" increase the spam score.</div>')
+
+        predict_btn.click(fn=ui_predict, inputs=msg, outputs=result)
+        clear_btn.click(lambda: "", [], [msg])
+        msg.submit(fn=ui_predict, inputs=msg, outputs=result)
+
+if __name__ == "__main__":
+    demo.launch()