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import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import io
import ast
from PIL import Image, ImageDraw
import google.generativeai as genai
import traceback
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')
# Read uploaded file
file_path = file.name
df = pd.read_csv(file_path) if file_path.endswith('.csv') else pd.read_excel(file_path)
# Generate visualization code
response = model.generate_content(f"""
Analyze the following dataset and instructions:
Data columns: {list(df.columns)}
Instructions: {instructions}
Based on this, create 3 appropriate visualizations. For each visualization, provide:
1. A title
2. The most suitable plot type (choose from: bar, line, scatter, hist)
3. The column to use for the x-axis
4. The column(s) to use for the y-axis (can be a list for multiple columns, or None for histograms)
5. Any necessary data preprocessing steps (e.g., grouping, sorting, etc.)
Return your response as a Python list of dictionaries:
[
{{"title": "...", "plot_type": "...", "x": "...", "y": "...", "preprocessing": "..."}},
{{"title": "...", "plot_type": "...", "x": "...", "y": "...", "preprocessing": "..."}},
{{"title": "...", "plot_type": "...", "x": "...", "y": "...", "preprocessing": "..."}}
]
""")
# Extract code block safely
code_block = response.text
if '```python' in code_block:
code_block = code_block.split('```python')[1].split('```')[0].strip()
elif '```' in code_block:
code_block = code_block.split('```')[1].strip()
print("Generated code block:")
print(code_block)
plots = ast.literal_eval(code_block)
# Generate visualizations
images = []
for plot in plots[:3]: # Ensure max 3 plots
fig, ax = plt.subplots(figsize=(10, 6))
# Apply preprocessing
plot_df = df.copy()
if 'Group data by' in plot['preprocessing']:
group_by = plot['x']
agg_column = plot['y'][0] if isinstance(plot['y'], list) else plot['y']
plot_df = plot_df.groupby(group_by)[agg_column].sum().reset_index()
if 'Sort' in plot['preprocessing']:
plot_df = plot_df.sort_values(by=plot['y'][0] if isinstance(plot['y'], list) else plot['y'], ascending=False)
if 'Filter to keep only the top 5' in plot['preprocessing']:
plot_df = plot_df.head(5)
if plot['plot_type'] == 'bar':
plot_df.plot(kind='bar', x=plot['x'], y=plot['y'], ax=ax)
elif plot['plot_type'] == 'line':
plot_df.plot(kind='line', x=plot['x'], y=plot['y'], ax=ax)
elif plot['plot_type'] == 'scatter':
plot_df.plot(kind='scatter', x=plot['x'], y=plot['y'], ax=ax)
elif plot['plot_type'] == 'hist':
plot_df[plot['x']].hist(ax=ax)
ax.set_title(plot['title'])
ax.set_xlabel(plot['x'])
ax.set_ylabel(plot['y'][0] if isinstance(plot['y'], list) else plot['y'])
plt.tight_layout()
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img = Image.open(buf)
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))
except Exception as e:
error_message = f"Error: {str(e)}\n\nTraceback:\n{traceback.format_exc()}"
print(error_message) # Print to console for debugging
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] * 3
with gr.Blocks(theme=gr.themes.Default()) as demo:
gr.Markdown("# 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...")
api_key = gr.Textbox(label="Gemini API Key", type="password")
submit = gr.Button("Generate Insights", variant="primary")
output_images = [gr.Image(label=f"Visualization {i+1}") for i in range(3)]
submit.click(
process_file,
inputs=[file, instructions, api_key],
outputs=output_images
)
if __name__ == "__main__":
demo.launch() |