autoinference

Your task is to develop a mini-application named 'AutoInsight' using the Python package 'autoinference'. This tool aims to simplify the process of performing automatic inference on datasets without requiring deep knowledge of machine learning techniques. Your application will serve as a user-friendly interface where users can upload their dataset, select the type of inference they wish to perform, and receive insightful results automatically.

Core Features:
1. User Interface: Develop a simple, intuitive web-based UI using Flask or Streamlit where users can upload their CSV files.
2. Dataset Processing: Implement functionality within 'autoinference' to preprocess the uploaded data, handling missing values, encoding categorical variables, etc.
3. Inference Selection: Allow users to choose between different types of inferences such as classification, regression, clustering, etc.
4. Automatic Model Selection: Utilize 'autoinference' to automatically select the best model based on the provided dataset and chosen inference type.
5. Results Presentation: Display the results of the inference in an easily understandable format, including metrics like accuracy, precision, recall, etc., and visualizations if applicable.
6. Documentation: Provide comprehensive documentation detailing how to use 'AutoInsight', including examples of input datasets and expected outputs.

Utilization of 'autoinference':
- Use 'autoinference' to handle the preprocessing of datasets, ensuring that the data is ready for analysis without manual intervention.
- Leverage 'autoinference's ability to automatically infer the best model for the given task, reducing the need for users to manually experiment with different algorithms.
- Integrate 'autoinference's reporting capabilities to generate detailed reports on the performance of the selected models, making it easy for users to interpret the results.