Update app.py

app.py

@@ -1,59 +1,83 @@
 import gradio as gr
 import pandas as pd
-import matplotlib.pyplot as plt
-import fcsparser
+import numpy as np
+from shapely.geometry import Point, Polygon
 
-# Function to parse the FCS file
-def parse_fcs(file):
-    if file is None:
-        return pd.DataFrame()
-    try:
-        meta, data = fcsparser.parse(file.name)
-        return pd.DataFrame(data)
-    except Exception as e:
-        raise gr.Error(f"Error parsing .fcs file: {str(e)}")
+def load_csv(file):
+    return pd.read_csv(file.name)
+
+def update_dropdowns(df):
+    return gr.Dropdown(choices=df.columns.tolist()), gr.Dropdown(choices=df.columns.tolist())
 
-# Function to populate the dropdown choices
-def populate_dropdowns(df):
-    if df is None or df.empty:
-        return gr.Dropdown.update(choices=[]), gr.Dropdown.update(choices=[])
-    columns = df.columns.tolist()
-    return gr.Dropdown.update(choices=columns), gr.Dropdown.update(choices=columns)
+def create_plot(df, x_col, y_col):
+    if df is None or x_col is None or y_col is None:
+        return None
+    return gr.ScatterPlot.update(
+        value=pd.DataFrame({x_col: df[x_col], y_col: df[y_col]}),
+        x=x_col,
+        y=y_col,
+        title=f"{x_col} vs {y_col}",
+        interactive=True,
+        tooltip=[x_col, y_col]
+    )
 
-# Function to plot the data
-def plot_fcs(df, x, y):
-    if df is None or df.empty or x not in df.columns or y not in df.columns:
-        fig = plt.figure()
-        plt.text(0.5, 0.5, "Please select valid columns.", ha='center', va='center')
-        plt.axis('off')
-        return fig
-    try:
-        fig = plt.figure(figsize=(10, 6))
-        plt.scatter(df[x], df[y], alpha=0.5)
-        plt.xlabel(x)
-        plt.ylabel(y)
-        plt.title(f"{x} vs {y}")
-        return fig
-    except Exception as e:
-        raise gr.Error(f"Error plotting data: {str(e)}")
+def find_points_in_polygon(selected_data, df, x_col, y_col):
+    if not selected_data or not x_col or not y_col:
+        return pd.DataFrame()
+        
+    # Extract polygon points from selection
+    polygon_points = [(point["x"], point["y"]) for point in selected_data["points"]]
+    
+    # Create Shapely objects
+    polygon = Polygon(polygon_points)
+    points = [Point(row[x_col], row[y_col]) for _, row in df.iterrows()]
+    
+    # Find points inside polygon
+    inside_indices = [i for i, point in enumerate(points) if polygon.contains(point)]
+    
+    return df.iloc[inside_indices]
 
-# Build the Gradio app
 with gr.Blocks() as demo:
-    gr.Markdown("<h1>FCS File Uploader and Plotter</h1>")
+    gr.Markdown("## CSV Data Explorer with Polygon Selection")
     
-    file = gr.File(label="Upload FCS File", file_types=[".fcs"])
+    # Store DataFrame and polygon points
     df_state = gr.State()
+    polygon_state = gr.State([])
     
     with gr.Row():
-        x_axis = gr.Dropdown(label="Select X-axis")
-        y_axis = gr.Dropdown(label="Select Y-axis")
+        csv_upload = gr.File(label="Upload CSV", file_types=[".csv"])
+        x_col = gr.Dropdown(label="X Column")
+        y_col = gr.Dropdown(label="Y Column")
     
-    plot = gr.Plot()
+    plot = gr.ScatterPlot(interactive=True, label="Scatter Plot")
+    results = gr.DataFrame(label="Points in Polygon")
     
     # Event handlers
-    file.change(parse_fcs, file, df_state)
-    file.change(populate_dropdowns, df_state, [x_axis, y_axis])
-    x_axis.change(plot_fcs, [df_state, x_axis, y_axis], plot)
-    y_axis.change(plot_fcs, [df_state, x_axis, y_axis], plot)
+    csv_upload.upload(
+        load_csv, 
+        inputs=csv_upload,
+        outputs=df_state
+    ).then(
+        update_dropdowns,
+        inputs=df_state,
+        outputs=[x_col, y_col]
+    )
+    
+    x_col.change(
+        create_plot,
+        inputs=[df_state, x_col, y_col],
+        outputs=plot
+    )
+    y_col.change(
+        create_plot,
+        inputs=[df_state, x_col, y_col],
+        outputs=plot
+    )
+    
+    plot.select(
+        find_points_in_polygon,
+        inputs=[df_state, x_col, y_col],
+        outputs=results
+    )
 
 demo.launch()