Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
 import io
-import json
 from PIL import Image, ImageDraw
 import google.generativeai as genai
 import traceback
@@ -11,7 +10,7 @@ def process_file(file, instructions, api_key):
     try:
         # Initialize Gemini
         genai.configure(api_key=api_key)
-        model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
+        model = genai.GenerativeModel('gemini-pro')
         
         # Read uploaded file
         file_path = file.name
@@ -29,24 +28,23 @@ def process_file(file, instructions, api_key):
         2. The most suitable plot type (choose from: bar, line, scatter, hist)
         3. The column to use for the x-axis
         4. The column to use for the y-axis (use None for histograms)
-        5. A brief explanation of why this visualization is appropriate
 
-        Return your response as a JSON string in this format:
+        Return your response as a Python list of tuples:
         [
-            {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "explanation": "..."}},
-            {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "explanation": "..."}},
-            {{"title": "...", "plot_type": "...", "x": "...", "y": "...", "explanation": "..."}}
+            ("Title 1", "plot_type1", "x_column1", "y_column1"),
+            ("Title 2", "plot_type2", "x_column2", "y_column2"),
+            ("Title 3", "plot_type3", "x_column3", "y_column3")
         ]
         """
         
         response = model.generate_content(prompt)
-        plots = json.loads(response.text)
+        plots = eval(response.text)
         
         # Generate visualizations
         images = []
         for plot in plots:
             fig, ax = plt.subplots(figsize=(10, 6))
-            title, plot_type, x, y = plot['title'], plot['plot_type'], plot['x'], plot['y']
+            title, plot_type, x, y = plot
             
             if plot_type == 'bar':
                 df.plot(kind='bar', x=x, y=y, ax=ax)
@@ -66,10 +64,10 @@ def process_file(file, instructions, api_key):
             plt.savefig(buf, format='png')
             buf.seek(0)
             img = Image.open(buf)
-            images.append((img, plot['explanation']))
+            images.append(img)
             plt.close(fig)
 
-        return images if len(images) == 3 else images + [(Image.new('RGB', (800, 600), (255,255,255)), "")]*(3-len(images))
+        return images if len(images) == 3 else images + [Image.new('RGB', (800, 600), (255,255,255))]*(3-len(images))
 
     except Exception as e:
         error_message = f"Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
@@ -77,7 +75,7 @@ def process_file(file, instructions, api_key):
         error_image = Image.new('RGB', (800, 400), (255, 255, 255))
         draw = ImageDraw.Draw(error_image)
         draw.text((10, 10), error_message, fill=(255, 0, 0))
-        return [(error_image, "An error occurred")] * 3
+        return [error_image] * 3
 
 with gr.Blocks(theme=gr.themes.Default()) as demo:
     gr.Markdown("# Data Analysis Dashboard")
@@ -90,12 +88,11 @@ with gr.Blocks(theme=gr.themes.Default()) as demo:
     submit = gr.Button("Generate Insights", variant="primary")
     
     output_images = [gr.Image(label=f"Visualization {i+1}") for i in range(3)]
-    output_texts = [gr.Textbox(label=f"Explanation {i+1}") for i in range(3)]
 
     submit.click(
         process_file,
         inputs=[file, instructions, api_key],
-        outputs=output_images + output_texts
+        outputs=output_images
     )
 
 if __name__ == "__main__":