Update app.py

app.py

@@ -2,92 +2,73 @@ import gradio as gr
 import pandas as pd
 import matplotlib.pyplot as plt
 import io
-from PIL import Image, ImageDraw, ImageFont
-import traceback
+import ast
+from PIL import Image
+import google.generativeai as genai
 
 def process_file(api_key, file, instructions):
     try:
+        # Initialize Gemini
+        genai.configure(api_key=api_key)
+        model = genai.GenerativeModel('gemini-pro')
+        
         # Read uploaded file
-        if file.name.endswith('.csv'):
-            df = pd.read_csv(file.name)
-        elif file.name.endswith('.xlsx'):
-            df = pd.read_excel(file.name)
+        file_path = file.name  # Get full file path
+        if file_path.endswith('.csv'):
+            df = pd.read_csv(file_path)
         else:
-            raise ValueError("Unsupported file format")
-
-        # Generate sample visualizations (replace with actual logic)
-        fig1, ax1 = plt.subplots()
-        df.plot(kind='bar', ax=ax1)
-        ax1.set_title("Sample Bar Chart")
+            df = pd.read_excel(file_path)
         
-        fig2, ax2 = plt.subplots()
-        df.plot(kind='line', ax=ax2)
-        ax2.set_title("Sample Line Chart")
+        # Generate visualization code based on instructions
+        columns = list(df.columns)
+        response = model.generate_content(f"""
+            Create 3 matplotlib visualization codes based on: {instructions}
+            Data columns: {columns}
+            Return only Python code as: [('title','plot_type','x','y'), ...]
+            Allowed plot_types: bar, line, scatter, hist
+            Use only DataFrame 'df' and these exact variable names.
+        """)
         
-        fig3, ax3 = plt.subplots()
-        df.plot(kind='hist', ax=ax3)
-        ax3.set_title("Sample Histogram")
-
-        # Convert plots to PIL Images
-        def fig_to_image(fig):
+        # Parse and validate generated code
+        plots = ast.literal_eval(response.text.split('```')[-2].strip('python\n '))
+        if len(plots) != 3:
+            raise ValueError("Exactly 3 visualizations required")
+            
+        # Generate plots
+        images = []
+        for plot in plots:
+            fig = plt.figure()
+            title, plot_type, x, y = plot
+            
+            if plot_type == 'bar':
+                df.plot.bar(x=x, y=y, ax=plt.gca())
+            elif plot_type == 'line':
+                df.plot.line(x=x, y=y, ax=plt.gca())
+            elif plot_type == 'scatter':
+                df.plot.scatter(x=x, y=y, ax=plt.gca())
+            elif plot_type == 'hist':
+                df[y].hist(ax=plt.gca())
+            
+            plt.title(title)
             buf = io.BytesIO()
-            fig.savefig(buf, format='png')
+            fig.savefig(buf, format='png', bbox_inches='tight')
             buf.seek(0)
-            return Image.open(buf)
+            images.append(Image.open(buf))
+            plt.close()
 
-        return [
-            fig_to_image(fig1),
-            fig_to_image(fig2),
-            fig_to_image(fig3)
-        ]
+        return images
 
     except Exception as e:
-        error_message = f"{str(e)}\n{traceback.format_exc()}"
-        return [generate_error_image(error_message)] * 3
-
-def generate_error_image(message):
-    """Create error indication image with message"""
-    try:
-        img = Image.new('RGB', (800, 400), color=(255, 255, 255))
-        draw = ImageDraw.Draw(img)
-        font = ImageFont.load_default()
-        
-        # Wrap text
-        lines = []
-        for line in message.split('\n'):
-            if len(line) > 80:
-                lines.extend([line[i:i+80] for i in range(0, len(line), 80)])
-            else:
-                lines.append(line)
-
-        y_text = 10
-        for line in lines[:20]:  # Limit to 20 lines
-            draw.text((10, y_text), line, font=font, fill=(255, 0, 0))
-            y_text += 15
+        error_image = Image.new('RGB', (800, 100), (255, 255, 255))
+        draw = ImageDraw.Draw(error_image)
+        draw.text((10, 40), f"Error: {str(e)}", fill=(255, 0, 0))
+        return [error_image] * 3
 
-        return img
-    except Exception as e:
-        return Image.new('RGB', (800, 400), color=(255, 255, 255))
-
-# Gradio interface
 with gr.Blocks(theme=gr.themes.Default(spacing_size="lg")) as demo:
-    gr.Markdown("# AutoData Visualizer")
+    gr.Markdown("# Data Analysis Dashboard")
     
     with gr.Row():
         api_key = gr.Textbox(label="Gemini API Key", type="password")
-        file = gr.File(label="Upload Data File", file_types=[".csv", ".xlsx"])
-    
-    instructions = gr.Textbox(label="Visualization Instructions")
-    submit = gr.Button("Generate Insights", variant="primary")
+        file = gr.File(label="Upload Dataset", file_types=[".csv", ".xlsx"])
     
-    with gr.Row():
-        outputs = [gr.Image(label=f"Visualization {i+1}", width=600) for i in range(3)]
-
-    submit.click(
-        process_file,
-        inputs=[api_key, file, instructions],
-        outputs=outputs
-    )
-
-if __name__ == "__main__":
-    demo.launch()
+    instructions = gr.Textbox(label="Analysis Instructions