Update app.py

app.py

@@ -1,10 +1,5 @@
-import gradio as gr
-import pandas as pd
-import matplotlib.pyplot as plt
-import io
-import google.generativeai as genai
+import base64
 
-# Function to process the file and generate visualizations
 def process_file(api_key, file, instructions):
     # Set up Gemini API
     genai.configure(api_key=api_key)
@@ -26,27 +21,28 @@ def process_file(api_key, file, instructions):
     response = model.generate_content(prompt)
     suggestions = response.text.split('\n')
 
-    # Generate visualizations (placeholder - you'll need to implement actual visualization logic)
+    # Generate visualizations
     visualizations = []
     for i, suggestion in enumerate(suggestions[:3]):
-        plt.figure()
+        plt.figure(figsize=(10, 6))
         plt.title(f"Visualization {i+1}")
-        plt.text(0.5, 0.5, suggestion, ha='center', va='center')
+        plt.text(0.5, 0.5, suggestion, ha='center', va='center', wrap=True)
         buf = io.BytesIO()
         plt.savefig(buf, format='png')
         buf.seek(0)
-        visualizations.append(buf)
+        img_str = base64.b64encode(buf.getvalue()).decode()
+        visualizations.append(f"data:image/png;base64,{img_str}")
+        plt.close()
 
     return visualizations
 
-# Gradio interface
+# Update the Gradio interface
 with gr.Blocks() as demo:
     gr.Markdown("Data Visualization with Gemini")
     api_key = gr.Textbox(label="Enter Gemini API Key")
     file = gr.File(label="Upload Excel or CSV file")
     instructions = gr.Textbox(label="Optional visualization instructions")
     submit = gr.Button("Generate Visualizations")
-    progress = gr.Progress()
     outputs = [gr.Image(label=f"Visualization {i+1}") for i in range(3)]
     
     submit.click(
@@ -55,4 +51,4 @@ with gr.Blocks() as demo:
         outputs=outputs
     )
 
-demo.launch()
+demo.launch(share=True)