Update README.md

README.md

@@ -1,99 +1,14 @@
-# Omnibin
-
-A Python package for generating comprehensive binary classification reports with visualizations and confidence intervals.
-
-## Try it Online
-
-You can try Omnibin directly in your browser through our [Hugging Face Space](https://felipekitamura-omnibin.hf.space).
-
-## Installation
-
-```bash
-pip install omnibin
-```
-
-## Usage
-
-```python
-import pandas as pd
-from omnibin import generate_binary_classification_report, ColorScheme
-
-# Load your data
-data = pd.read_csv("data/scores.csv")
-y_true = data['y_true'].values
-y_scores = data['y_pred'].values
-
-# Generate comprehensive classification report
-report_path = generate_binary_classification_report(
-    y_true=y_true,                    # Array of true binary labels (0 or 1)
-    y_scores=y_scores,                # Array of predicted probabilities or scores
-    output_path="classification_report.pdf",  # Path to save the PDF report
-    n_bootstrap=1000,                 # Number of bootstrap iterations for confidence intervals
-    random_seed=42,                   # Random seed for reproducibility
-    dpi=300,                          # DPI for plot resolution
-    color_scheme=ColorScheme.DEFAULT  # Color scheme for plots (DEFAULT, MONOCHROME, or VIBRANT)
-)
-```
-
-## Input Format
-
-The input data should be provided as:
-- `y_true`: Array of true binary labels (0 or 1)
-- `y_pred`: Array of predicted probabilities or scores
-
-## Features
-
-- Generates a comprehensive PDF report with:
-  - ROC curve with confidence bands
-  - Precision-Recall curve with confidence bands
-  - Metrics vs. threshold plots
-  - Confusion matrix at optimal threshold
-  - Calibration plot
-  - Summary table with confidence intervals
-- Calculates optimal threshold using Youden's J statistic
-- Provides confidence intervals using bootstrapping
-- Supports both probability and score-based predictions
-
-## Metrics Included
-
-- Accuracy
-- Sensitivity (Recall)
-- Specificity
-- Positive Predictive Value (Precision)
-- Matthews Correlation Coefficient
-- F1 Score
-- AUC-ROC
-- AUC-PR
-
-All metrics include 95% confidence intervals calculated through bootstrapping.
-
-## Output
-
-The package generates a PDF report containing:
-1. ROC and Precision-Recall curves with confidence bands
-2. Metrics plotted across different thresholds
-3. Confusion matrix at the optimal threshold
-4. Calibration plot
-5. Summary table with all metrics and their confidence intervals 
-
-## Example
-
-Here are examples of the visualizations generated by Omnibin:
-
-### ROC and Precision-Recall Curves
-![ROC and PR Curves](results/plots/roc_pr.png)
-
-### Metrics vs Threshold
-<img src="results/plots/metrics_threshold.png">
-
-### Confusion Matrix
-<img src="results/plots/confusion_matrix.png">
-
-### Calibration Plot
-<img src="results/plots/calibration.png">
-
-### Prediction Distribution
-<img src="results/plots/prediction_distribution.png">
-
-### Metrics Summary
-![Metrics Summary](results/plots/metrics_summary.png) 
+---
+title: Omnibin
+emoji: ⚡
+colorFrom: pink
+colorTo: yellow
+sdk: gradio
+sdk_version: 5.29.0
+app_file: app.py
+pinned: false
+license: mit
+short_description: A Python package for generating comprehensive binary classif
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference