Update app.py

app.py

@@ -4,6 +4,18 @@ from sklearn.preprocessing import MinMaxScaler, LabelEncoder
 from sklearn.metrics import accuracy_score
 from concrete.ml.sklearn import XGBClassifier as ConcreteXGBClassifier
 
+
+# Örnek dosyalar
+SAMPLE_DIR = "Sample Inputs (Excel)"
+EXAMPLE_XLSX = [
+    f"{SAMPLE_DIR}/ROYAL_2019.xlsx",
+    f"{SAMPLE_DIR}/MARKA_2016.xlsx",
+    f"{SAMPLE_DIR}/THYAO_2023.xlsx",
+    f"{SAMPLE_DIR}/TTRAK_2024.xlsx"
+]
+
+
+
 SELECTED_FEATS = [
     "Cari Oran",
     "Dönen Varlıklar / Aktif (%)",
@@ -190,13 +202,23 @@ def predict_opinion(excel_file: gr.File):
     labels = encoder.inverse_transform(y_pred)
     return pd.DataFrame({"Tahmin Görüş Tipi": labels})
 
-# ------------------------ Gradio UI ------------------------
+# Gradio UI
 with gr.Blocks(theme=gr.themes.Default(primary_hue="blue")) as demo:
     gr.Markdown("# Denetçi Görüşü Tahmin Uygulaması")
-    file_in = gr.File(file_types=[".xlsx", ".xls", ".xlsm"], label="Excel Yükleyin")
-    btn     = gr.Button("Tahmin Et", variant="primary")
-    out_df  = gr.Dataframe(wrap=True, show_label=False)
+
+    with gr.Row():
+        file_in = gr.File(file_types=[".xlsx", ".xls", ".xlsm"], label="Excel Yükleyin")
+        btn     = gr.Button("Tahmin Et", variant="primary")
+
+    gr.Examples(
+        examples=EXAMPLE_XLSX,
+        inputs=file_in,
+        label="Örnek Dosyayı Deneyin",
+        cache_examples=False,
+    )
+
+    out_df = gr.Dataframe(wrap=True, show_label=False, row_count=0)
     btn.click(predict_opinion, file_in, out_df)
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()