Fix links

app.py

@@ -6,13 +6,13 @@ st.title('Is your model degrading?')
 st.caption('### :violet[_Estimate_] the performance of an ML model. :violet[_Without ground truth_].')
 
 st.markdown("""
-If you have been previously exposed to concepts like [covariate shift or concept drift]('https://www.nannyml.com/blog/types-of-data-shift'),
+If you have been previously exposed to concepts like [covariate shift or concept drift](https://www.nannyml.com/blog/types-of-data-shift),
 you may be aware that changes in the distribution of
 the production data can affect the model's performance.
 """)
             
 st.markdown("""Recently a paper from MIT, Harvard, and other institutions showed how [91% of their ML models
-experiments degraded]('https://www.nannyml.com/blog/91-of-ml-perfomance-degrade-in-time') in time.""")
+experiments degraded](https://www.nannyml.com/blog/91-of-ml-perfomance-degrade-in-time) in time.""")
             
 st.markdown("""Typically, we need access to ground truth to know if a model is degrading.
 But most of the time, getting new labeled data is expensive, time-consuming, or impossible.
@@ -45,7 +45,7 @@ will continue to be good on production data?
 st.markdown("#### Estimating the Model Performance")
 st.markdown("""
 Instead of waiting for ground truth, we can use NannyML's 
-[CBPE]("https://nannyml.readthedocs.io/en/stable/tutorials/performance_estimation/binary_performance_estimation/standard_metric_estimation.html")
+[CBPE](https://nannyml.readthedocs.io/en/stable/tutorials/performance_estimation/binary_performance_estimation/standard_metric_estimation.html)
 method to estimate the performance of an ML model.
 
 CBPE's trick is to use the confidence scores of the ML model. It calibrates the scores to turn them into actual probabilities.
@@ -103,6 +103,6 @@ st.divider()
 st.markdown("""Created by [santiviquez](https://twitter.com/santiviquez) from NannyML.""")
 
 st.markdown("""
-NannyML is an open-source library for post-deployment data science. Leave us a 🌟 on [GitHub]("https://github.com/NannyML/nannyml")
-            or [check our docs]('https://nannyml.readthedocs.io/en/stable/landing_page.html') to learn more.
+NannyML is an open-source library for post-deployment data science. Leave us a 🌟 on [GitHub](https://github.com/NannyML/nannyml)
+            or [check our docs](https://nannyml.readthedocs.io/en/stable/landing_page.html) to learn more.
 """)