Update app.py

app.py

@@ -1,85 +1,42 @@
 import gradio as gr
 import pandas as pd
 import plotly.express as px
-from sklearn.datasets import make_blobs
-
-def create_dataset():
-    # Örnek veri oluştur
-    data, _ = make_blobs(n_samples=300, centers=3, n_features=2, random_state=42)
-    df = pd.DataFrame(data, columns=['X', 'Y'])
-    df['Category'] = ['A', 'B', 'C'] * 100
-    return df
 
 def update_columns(csv_file):
     if csv_file is None:
-        return [gr.Dropdown(choices=[])] * 2
+        return gr.Dropdown(choices=[]), gr.Dropdown(choices=[])
     df = pd.read_csv(csv_file.name)
     numeric_cols = df.select_dtypes(include=['number']).columns.tolist()
     return gr.Dropdown(choices=numeric_cols), gr.Dropdown(choices=numeric_cols)
 
-def create_plot(csv_file, x_col, y_col):
+def create_scatter_plot(csv_file, x_col, y_col):
     if csv_file is None or x_col is None or y_col is None:
-        df = create_dataset()
-        x_col, y_col = 'X', 'Y'
-    else:
-        df = pd.read_csv(csv_file.name)
+        return None, None
     
-    fig = px.scatter(
-        df, 
-        x=x_col, 
-        y=y_col,
-        color='Category' if 'Category' in df.columns else None,
-        title="Interactive Scatter Plot with Polygon Selection",
-        hover_data=df.columns.tolist()
-    )
+    df = pd.read_csv(csv_file.name)
     
-    fig.update_layout(
-        dragmode='drawclosedpath',  # Polygon çizim modu
-        newshape=dict(line_color='cyan'),  # Çizim rengi
-        margin=dict(l=20, r=20, b=20, t=40)
+    # Gradio ScatterPlot için uygun formatta veri hazırla
+    return (
+        gr.ScatterPlot(
+            value=df,
+            x=x_col,
+            y=y_col,
+            tooltip=df.columns.tolist(),
+            title="Interactive Scatter Plot",
+            color_legend_title="Values",
+            interactive=True,
+            brush_radius=5
+        ),
+        df
     )
-    
-    config = {
-        'modeBarButtonsToAdd': [
-            'drawclosedpath',
-            'eraseshape'
-        ]
-    }
-    return fig, df
 
-def get_selected_points(plot_data, df):
-    if plot_data is None or 'range' in plot_data:
+def get_selected_points(evt: gr.SelectData, df):
+    if evt is None or df is None:
         return pd.DataFrame()
     
-    selected_points = []
-    for shape in plot_data.get('shapes', []):
-        if shape['type'] == 'path':
-            x_path = shape['path'].split('L')
-            polygon_points = [tuple(map(float, point.split(','))) for point in x_path]
-            
-            # Polygon içinde kalan noktaları bul
-            for idx, row in df.iterrows():
-                if is_point_in_polygon((row['X'], row['Y']), polygon_points):
-                    selected_points.append(idx)
-    
-    return df.iloc[selected_points] if selected_points else pd.DataFrame()
-
-def is_point_in_polygon(point, polygon):
-    # Ray casting algoritması ile nokta-polygon çakışma kontrolü
-    x, y = point
-    inside = False
-    n = len(polygon)
-    
-    for i in range(n):
-        j = (i + 1) % n
-        xi, yi = polygon[i]
-        xj, yj = polygon[j]
-        
-        intersect = ((yi > y) != (yj > y)) and (x < (xj - xi) * (y - yi) / (yj - yi) + xi)
-        if intersect:
-            inside = not inside
-            
-    return inside
+    # Seçilen indeksleri kullanarak verileri filtrele
+    selected_indices = [i["index"] for i in evt.selected]
+    return df.iloc[selected_indices]
 
 with gr.Blocks() as app:
     gr.Markdown("## 🔷 Polygon ile Veri Seçimi")
@@ -90,7 +47,7 @@ with gr.Blocks() as app:
         y_dropdown = gr.Dropdown(label="Y Ekseni")
     
     with gr.Row():
-        plot = gr.Plot(label="Scatter Plot")
+        scatter_plot = gr.ScatterPlot(interactive=True, brush_radius=5)
         selected_table = gr.DataFrame(label="Seçilen Veriler")
     
     df_state = gr.State()
@@ -102,18 +59,18 @@ with gr.Blocks() as app:
     )
     
     x_dropdown.change(
-        fn=create_plot,
+        fn=create_scatter_plot,
         inputs=[csv_upload, x_dropdown, y_dropdown],
-        outputs=[plot, df_state]
+        outputs=[scatter_plot, df_state]
     )
     
     y_dropdown.change(
-        fn=create_plot,
+        fn=create_scatter_plot,
         inputs=[csv_upload, x_dropdown, y_dropdown],
-        outputs=[plot, df_state]
+        outputs=[scatter_plot, df_state]
     )
     
-    plot.select(
+    scatter_plot.select(
         fn=get_selected_points,
         inputs=df_state,
         outputs=selected_table