Update model artifacts

app.py

@@ -2,43 +2,40 @@
 import gradio as gr
 import joblib
 import json
-import os
 from src.preprocessing import clean_text
 
-model = joblib.load("saved_model.joblib")
-vectorizer = joblib.load("vectorizer.joblib")
+model = joblib.load("model/saved_model.joblib")
+vectorizer = joblib.load("model/vectorizer.joblib")
 
 def predict(text):
     clean = clean_text(text)
     vec = vectorizer.transform([clean])
     pred = model.predict(vec)[0]
-    proba = None
-    if hasattr(model, "predict_proba"):
-        proba = model.predict_proba(vec)[0]
-        proba = { "Aman": round(proba[0]*100, 2), "Judi": round(proba[1]*100, 2) }
-
-    label = "🚫 Judi Online" if pred == "judi" or pred == 1 else "✅ Aman"
-    return {"Prediksi": label, "Probabilitas (%)": proba}
+    return "🚫 Judi Online" if pred == "judi" else "✅ Aman"
 
 def load_metrics():
-    path = "model/metrics_summary.json"
-    if os.path.exists(path):
-        with open(path) as f:
+    try:
+        with open("model/metrics_summary.json") as f:
             return json.load(f)
-    return {}
+    except:
+        return {"error": "Metrics not available. Run evaluation first."}
 
 with gr.Blocks() as demo:
     with gr.Tab("Inference"):
-        inp = gr.Textbox(lines=3, placeholder="Masukkan komentar...")
-        out = gr.JSON()
-        gr.Button("Prediksi").click(fn=predict, inputs=inp, outputs=out)
+        gr.Interface(
+            fn=predict,
+            inputs=gr.Textbox(lines=3, placeholder="Masukkan komentar..."),
+            outputs="text",
+            title="Deteksi Komentar Judi Online"
+        )
 
     with gr.Tab("Monitoring"):
+        gr.Markdown("### 📊 Model Performance Metrics")
         metrics = load_metrics()
-        if metrics:
-            gr.Markdown("### 📊 Model Performance")
-            gr.JSON(metrics)
-        if os.path.exists("model/confusion_matrix.png"):
+        if "error" in metrics:
+            gr.Label(metrics)
+        else:
+            gr.Label({k: round(v, 4) for k, v in metrics.items()})
             gr.Image("model/confusion_matrix.png", label="Confusion Matrix")
 
 if __name__ == "__main__":

requirements.txt

@@ -9,5 +9,4 @@ fastapi
 gradio
 pytest
 pytest-cov
-os
-json
+seaborn

saved_model.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0d2b3207c279e85fedb3b2040e9e5d087fc0d3efc92c5cdb700901de28c1d13a
-size 1339
+oid sha256:e56003c33afe3ca76111c4e09e0f61709b38e4be69d156f2ce7aa620f3d5f787
+size 1355

vectorizer.joblib

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:a31b94f8ea321f5820b83e63ed43f178c474bf7f1f704e65a269dead777238b5
-size 3719
+oid sha256:0c2f0281667ac90830c8763c894f12da4fab4c2b5bdce2e3e0971c777130b6f8
+size 2966