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Pearson correlation coefficient and p-value for testing non-correlation.
The Pearson correlation coefficient measures the linear relationship between two datasets. The calculation of the p-value relies on the assumption that each dataset is normally distributed. Like other correlation coefficients, this one varies between -1 and +1 with 0 implying no correlation. Correlations of -1 or +1 imply an exact linear relationship. Positive correlations imply that as x increases, so does y. Negative correlations imply that as x increases, y decreases.
The p-value roughly indicates the probability of an uncorrelated system producing datasets that have a Pearson correlation at least as extreme as the one computed from these datasets.

README.md

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-title: Pearson Correlation Coefficient 
-emoji: 🤗 
+title: Pearson Correlation Coefficient
+emoji: 🤗
 colorFrom: blue
 colorTo: red
 sdk: gradio
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 app_file: app.py
 pinned: false
 tags:
-- evaluate
-- metric
+  - evaluate
+  - metric
+description: >-
+  Pearson correlation coefficient and p-value for testing non-correlation.
+
+  The Pearson correlation coefficient measures the linear relationship between
+  two datasets. The calculation of the p-value relies on the assumption that
+  each dataset is normally distributed. Like other correlation coefficients,
+  this one varies between -1 and +1 with 0 implying no correlation. Correlations
+  of -1 or +1 imply an exact linear relationship. Positive correlations imply
+  that as x increases, so does y. Negative correlations imply that as x
+  increases, y decreases.
+
+  The p-value roughly indicates the probability of an uncorrelated system
+  producing datasets that have a Pearson correlation at least as extreme as the
+  one computed from these datasets.
 ---
-
 # Metric Card for Pearson Correlation Coefficient (pearsonr)