File size: 3,273 Bytes
4fc79a4 ff768e2 4fc79a4 ccd7bab 4fc79a4 ccd7bab 1e2b302 4fc79a4 1e2b302 e6ca829 7d1e58a 4fc79a4 1e2b302 ccd7bab 4fc79a4 1e2b302 4fc79a4 e6ca829 ccd7bab e6ca829 1e2b302 e6ca829 1e2b302 e6ca829 1e2b302 e6ca829 1e2b302 4fc79a4 e6ca829 4fc79a4 1e2b302 4fc79a4 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 |
import gradio as gr
import pandas as pd
import matplotlib.pyplot as plt
import io
import base64
import google.generativeai as genai
import textwrap
def process_file(api_key, file, instructions):
# Set up Gemini API
genai.configure(api_key=api_key)
model = genai.GenerativeModel('gemini-2.5-pro-preview-03-25')
# Read the file
if file.name.endswith('.csv'):
df = pd.read_csv(file.name)
else:
df = pd.read_excel(file.name)
# Analyze data and get visualization suggestions from Gemini
data_description = df.describe().to_string()
columns_info = "\n".join([f"{col}: {df[col].dtype}" for col in df.columns])
prompt = f"""
Given this dataset:
Columns and types:
{columns_info}
Data summary:
{data_description}
User instructions: {instructions if instructions else 'No specific instructions provided.'}
Generate Python code for 3 different visualizations using matplotlib. Each visualization should be unique and provide insights into the data. Do not include any explanations or descriptions, only the Python code for each visualization. Ensure the code is not indented.
Format your response as:
# Visualization 1
# Your code here
# Visualization 2
# Your code here
# Visualization 3
# Your code here
"""
response = model.generate_content(prompt)
code = response.text.strip()
print("Generated code:")
print(code)
visualizations = []
for i, viz_code in enumerate(code.split("# Visualization")[1:4], 1):
plt.figure(figsize=(10, 6))
try:
# Remove any leading spaces from each line
cleaned_code = '\n'.join(line.strip() for line in viz_code.split('\n') if line.strip())
print(f"\nVisualization {i} code:")
print(cleaned_code)
exec(cleaned_code, {'df': df, 'plt': plt})
plt.title(f"Visualization {i}")
# Save the plot to a BytesIO object
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
img_str = base64.b64encode(buf.getvalue()).decode()
plt.close()
visualizations.append(f"data:image/png;base64,{img_str}")
except Exception as e:
print(f"Error in visualization {i}: {str(e)}")
visualizations.append(None)
# Ensure we always return 3 visualizations
while len(visualizations) < 3:
visualizations.append(None)
return visualizations
# Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Data Visualization with Gemini")
api_key = gr.Textbox(label="Enter Gemini API Key", type="password")
file = gr.File(label="Upload Excel or CSV file")
instructions = gr.Textbox(label="Optional visualization instructions")
submit = gr.Button("Generate Visualizations")
with gr.Row():
output1 = gr.Image(label="Visualization 1")
output2 = gr.Image(label="Visualization 2")
output3 = gr.Image(label="Visualization 3")
submit.click(
fn=process_file,
inputs=[api_key, file, instructions],
outputs=[output1, output2, output3],
show_progress=True,
)
demo.launch() |