Update app.py

app.py

@@ -1,80 +1,125 @@
 import gradio as gr
+import numpy as np
 import pandas as pd
-import plotly.express as px
+import plotly.graph_objects as go
+from fcsparser import parse
+from matplotlib.path import Path
 
-def update_columns(csv_file):
-    if csv_file is None:
-        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 parse_fcs(file):
+    _, data = parse(file.name, reformat_meta=True)
+    return data
 
-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:
-        return None, None
+def update_params(data):
+    if data is None: 
+        return [gr.Dropdown.update(choices=[])] * 2
+    return [gr.Dropdown.update(choices=data.columns.tolist())] * 2
+
+def create_plot(data, x_param, y_param):
+    if data is None or not x_param or not y_param:
+        return None
     
-    df = pd.read_csv(csv_file.name)
+    fig = go.Figure(data=go.Scattergl(
+        x=data[x_param],
+        y=data[y_param],
+        mode='markers',
+        marker=dict(size=2, opacity=0.5)
+    ))
     
-    # 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
+    fig.update_layout(
+        title=f"{x_param} vs {y_param}",
+        dragmode='drawclosedpath',
+        width=800,
+        height=600
     )
-
-def get_selected_points(evt: gr.SelectData, df):
-    if evt is None or df is None:
-        return pd.DataFrame()
     
-    # Seçilen indeksleri kullanarak verileri filtrele
-    selected_indices = [i["index"] for i in evt.selected]
-    return df.iloc[selected_indices]
+    return fig
 
-with gr.Blocks() as app:
-    gr.Markdown("## 🔷 Polygon ile Veri Seçimi")
+def apply_gate(data, x_param, y_param, fig):
+    if data is None or fig is None:
+        return "Önce veri yükleyin ve grafik oluşturun", None
     
-    with gr.Row():
-        csv_upload = gr.UploadButton(label="CSV Yükle", file_types=[".csv"])
-        x_dropdown = gr.Dropdown(label="X Ekseni")
-        y_dropdown = gr.Dropdown(label="Y Ekseni")
+    # Plotly şekil verisini çıkar
+    shapes = fig.get('layout', {}).get('shapes', [])
+    if not shapes:
+        return "Lütfen önce polygon çizin", None
     
-    with gr.Row():
-        scatter_plot = gr.ScatterPlot(interactive=True, brush_radius=5)
-        selected_table = gr.DataFrame(label="Seçilen Veriler")
+    # Son çizilen şekli al
+    last_shape = shapes[-1]
+    path = last_shape.get('path', '')
     
-    df_state = gr.State()
+    # Path verisini işle (M=move, L=line, Z=close)
+    vertices = []
+    for part in path.split(' '):
+        if part.startswith('M') or part.startswith('L'):
+            x, y = part[1:].split(',')
+            vertices.append((float(x), float(y)))
     
-    csv_upload.upload(
-        fn=update_columns,
-        inputs=csv_upload,
-        outputs=[x_dropdown, y_dropdown]
-    )
+    if len(vertices) < 3:
+        return "Geçerli bir polygon çizilmedi", None
     
-    x_dropdown.change(
-        fn=create_scatter_plot,
-        inputs=[csv_upload, x_dropdown, y_dropdown],
-        outputs=[scatter_plot, df_state]
-    )
+    # Matplotlib Path oluştur
+    polygon = Path(vertices)
+    points = np.vstack([data[x_param], data[y_param]]).T
     
-    y_dropdown.change(
-        fn=create_scatter_plot,
-        inputs=[csv_upload, x_dropdown, y_dropdown],
-        outputs=[scatter_plot, df_state]
-    )
+    # İçerde kalan noktaları bul
+    mask = polygon.contains_points(points)
+    gated_data = data[mask]
     
-    scatter_plot.select(
-        fn=get_selected_points,
-        inputs=df_state,
-        outputs=selected_table
+    # Sonuçları hazırla
+    stats = f"""
+    Toplam Hücre: {len(data):,}
+    Geçitlenen Hücre: {len(gated_data):,} (%{len(gated_data)/len(data)*100:.1f})
+    İlk 10 Hücre İndeksi: {gated_data.index[:10].tolist()}
+    """
+    
+    return stats, gated_data
+
+with gr.Blocks(title="FCS Analiz Uygulaması") as demo:
+    gr.Markdown("## FCS Dosya Analizi ve Hücre Geçitleme")
+    
+    with gr.Row():
+        with gr.Column():
+            file_input = gr.File(label="FCS Dosyası Yükle", type="file")
+            x_param = gr.Dropdown(label="X Parametresi")
+            y_param = gr.Dropdown(label="Y Parametresi")
+            plot_btn = gr.Button("Grafik Oluştur")
+            
+        with gr.Column():
+            plot_output = gr.Plot(label="Dağılım Grafiği")
+            gate_btn = gr.Button("Geçitlemeyi Uygula")
+            result_output = gr.Textbox(label="Sonuçlar")
+            data_output = gr.DataFrame(label="Geçitlenen Veriler", visible=False)
+
+    # State management
+    data_state = gr.State()
+    fig_state = gr.State()
+
+    # Event handlers
+    file_input.change(
+        fn=parse_fcs,
+        inputs=file_input,
+        outputs=data_state
+    ).then(
+        fn=update_params,
+        inputs=data_state,
+        outputs=[x_param, y_param]
+    )
+
+    plot_btn.click(
+        fn=create_plot,
+        inputs=[data_state, x_param, y_param],
+        outputs=[plot_output]
+    ).then(
+        lambda fig: fig,
+        inputs=plot_output,
+        outputs=fig_state
+    )
+
+    gate_btn.click(
+        fn=apply_gate,
+        inputs=[data_state, x_param, y_param, fig_state],
+        outputs=[result_output, data_output]
     )
 
 if __name__ == "__main__":
-    app.launch()
+    demo.launch()