2024-02-08 11:07:17 Publish script update

app.py

@@ -27,11 +27,9 @@ To do that we added following text to the query:
 
 {}
 
-When  measuring execution time we used `time.time()` result saved to variable  before making the call to API and compared it to `time.time()` result  after receiving the results. We used litellm python library for all of  the models which naturally adds some overhead compared to pure curl  calls.
+We used litellm python library for all of  the models which naturally adds some time overhead compared to pure curl calls
 
-In order to count tokens we split the output string by  whitespace `\w` regex character. For data which was impossible to obtain through the API, such as model sizes we only used official sources such  as developers' release blogs and their documentation.
-
-When it comes to pricing most providers charge per token count, while HuggingFace Endpoints allow the user to choose machine type and host the model repository on it. The user is then charged by the running time of the machine. In this project we attempted to use HF Endpoints as much as possible due to their popularity and transparency of how the model is executed.
+We also attempted to use HF Endpoints as much as possible due to their popularity and transparency of how the model is executed.
 """
 
 time_periods_explanation_df = pd.DataFrame({