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| import pandas as pd | |
| import gradio as gr | |
| from ydata_profiling import ProfileReport | |
| def generate_profile(file): | |
| # Load the uploaded file into a pandas dataframe | |
| df = pd.read_csv(file.name) | |
| # Generate the profile report | |
| profile = ProfileReport(df, title="Pandas Profiling Report") | |
| # Convert the profile report to a JSON format | |
| profile_json = profile.to_json() | |
| # Load the JSON into a pandas dataframe | |
| profile_data = json.loads(profile_json) | |
| df_profile = pd.json_normalize(profile_data) | |
| return df_profile | |
| # Create a Gradio interface | |
| iface = gr.Interface( | |
| fn=generate_profile, | |
| #inputs=["text", "text", "text"], | |
| inputs = ["file"], | |
| outputs=["text"], | |
| title="Agentville: Explore CSV files using autonomous agents", | |
| description="Generate insights in minutes on your own data", | |
| article="This Gradio app uses the CAMEL framework to initiate conversations across multiple autonomous agents for task-solving.", | |
| live=False, | |
| ) | |
| # Launch the Gradio app | |
| if __name__ == "__main__": | |
| iface.queue(max_size=20).launch(share=True,debug=True) |