Update app.py

app.py

@@ -1,4 +1,3 @@
-import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
 import io
@@ -7,6 +6,12 @@ from PIL import Image, ImageDraw
 import google.generativeai as genai
 import traceback
 import os
+from pywebio import start_server
+from pywebio.input import file_upload, input
+from pywebio.output import put_text, put_image, put_row, put_column, put_buttons, use_scope
+from pywebio.session import run_js
+import base64
+import threading
 
 def process_file(file, instructions):
     try:
@@ -16,8 +21,11 @@ def process_file(file, instructions):
         model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
         
         # Read uploaded file
-        file_path = file.name
-        df = pd.read_csv(file_path) if file_path.endswith('.csv') else pd.read_excel(file_path)
+        content = file['content']
+        if file['filename'].endswith('.csv'):
+            df = pd.read_csv(io.BytesIO(content))
+        else:
+            df = pd.read_excel(io.BytesIO(content))
         
         # Generate visualization code
         response = model.generate_content(f"""
@@ -115,22 +123,41 @@ def process_file(file, instructions):
         draw.text((10, 10), error_message, fill=(255, 0, 0))
         return [error_image] * 3
 
-with gr.Blocks(theme=gr.themes.Default()) as demo:
-    gr.Markdown("# Data Analysis Dashboard")
+def data_analysis_dashboard():
+    put_text("# Data Analysis Dashboard")
     
-    with gr.Row():
-        file = gr.File(label="Upload Dataset", file_types=[".csv", ".xlsx"])
-        instructions = gr.Textbox(label="Analysis Instructions", placeholder="Describe the analysis you want...")
+    with use_scope('form'):
+        put_row([
+            put_column([
+                file_upload("Upload Dataset", accept=[".csv", ".xlsx"], name="file"),
+                input("Analysis Instructions", type="text", placeholder="Describe the analysis you want...", name="instructions"),
+                put_buttons(['Generate Insights'], onclick=[lambda: generate_insights()])
+            ])
+        ])
     
-    submit = gr.Button("Generate Insights", variant="primary")
+    with use_scope('output'):
+        for i in range(3):
+            put_image(name=f'visualization_{i+1}')
+
+def generate_insights():
+    file = file_upload.files.get('file')
+    instructions = input.inputs.get('instructions')
+    
+    if not file or not instructions:
+        put_text("Please upload a file and provide instructions.")
+        return
+    
+    images = process_file(file, instructions)
     
-    output_images = [gr.Image(label=f"Visualization {i+1}") for i in range(3)]
+    for i, img in enumerate(images):
+        buffered = io.BytesIO()
+        img.save(buffered, format="PNG")
+        img_str = base64.b64encode(buffered.getvalue()).decode()
+        with use_scope(f'visualization_{i+1}', clear=True):
+            put_image(img_str, width='100%')
 
-    submit.click(
-        process_file,
-        inputs=[file, instructions],
-        outputs=output_images
-    )
+def main():
+    data_analysis_dashboard()
 
-if __name__ == "__main__":
-    demo.launch()
+if __name__ == '__main__':
+    start_server(main, host='0.0.0.0', port=7860, debug=True, cdn=False, auto_open_webbrowser=True)