2024-02-06 13:34:44 Publish script update

app.py

@@ -5,7 +5,7 @@ import pandas as pd
 import plotly
 from pandas.api.types import is_numeric_dtype
 
-from pipeline.config import LLMBoardConfig
+from pipeline.config import LLMBoardConfig, QueriesConfig
 
 README = """
 This projects compares different large language models and their providers for real time applications and mass data processing. 
@@ -17,7 +17,7 @@ For this version we chose English, Polish and Japanese languages, with Japanese
 We used the following prompt:
 
 ```
-Summarize me this text, the summary should be in {language}
+{}
 ```
 
 Where language variable is original language of the text as we wanted to avoid the model translating the text to English during summarization.
@@ -25,9 +25,7 @@ Where language variable is original language of the text as we wanted to avoid t
 The model was asked to return the output in three formats: markdown, json and function call. Note that currently function calls are only supported by Open AI API.
 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.
 
@@ -41,9 +39,16 @@ time_periods_explanation_df = pd.DataFrame({
     'hour_range': ["6-8", "9-11", "12-14", "15-17", "18-20", "21-23", "0-2", "3-5"]
 })
 
+queries_config = QueriesConfig()
+
+output_types_df = pd.DataFrame({
+    "Output Type": queries_config.query_template.keys(),
+    "Added text": queries_config.query_template.values()
+})
+
 summary_df: pd.DataFrame = pd.read_csv("data/2024-02-05 23:33:22.947120_summary.csv")
 time_of_day_comparison_df = pd.read_csv("data/2024-02-06 09:49:19.637072_time_of_day_comparison.csv")
-general_plots = pd.read_csv("data/2024-02-05 12:03:42.452218_general_plot.csv")
+general_plots = pd.read_csv("data/2024-02-06 13:25:55.133644_general_plot.csv")
 model_costs_df = pd.read_csv("data/2024-02-05 12:03:45.281624_model_costs.csv")
 
 
@@ -129,8 +134,8 @@ def snake_case_to_title(text):
     return " ".join(title_words)
 
 
-filter_textbox = gr.Textbox(label="Model name part")
-filter_button = gr.Button("Filter dataframes by model name")
+filter_textbox = gr.Textbox(label="Model name part", scale=2)
+filter_button = gr.Button("Filter dataframes by model name", scale=1)
 collapse_languages_button = gr.Button("Collapse languages")
 collapse_output_method_button = gr.Button("Collapse output method")
 last_textbox = 0
@@ -143,14 +148,17 @@ with gr.Blocks() as demo:
         filter_button.render()
     
     with gr.Tab("About this project"):
-        gr.Markdown(README)
+        gr.Markdown(README.format(
+            queries_config.base_query_template.replace("```", "'''"),
+            output_types_df.to_markdown(index=False)
+        ))
     with gr.Tab("General plots"):
         for index, row in general_plots.iterrows():
             plot = plotly.io.from_json(row["plot_json"])
             plot.update_layout(autosize=True)
-            gr.Plot(plot, label=row["description"], scale=1)
-            if pd.notna(row["comment"]):
-                gr.Markdown(str(row["comment"]))
+            gr.Plot(plot, label=row["header"], scale=1)
+            if pd.notna(row["description"]):
+                gr.Markdown(str(row["description"]))
     with gr.Tab("Output characteristics"):
         with gr.Row():
             collapse_languages_button.render()
@@ -163,6 +171,8 @@ with gr.Blocks() as demo:
         time_periods_explanation_ui = gr.DataFrame(dataframe_style(time_periods_explanation_df), label="Times of day ranges")
         gr.Markdown("""
 These measurements were made by testing the models using the same dataset as in the other comparisons every hour for 24 hours.
+                    
+Execution time refers to averaged time needed to execute one query.
 
 Hours and times of day in the table and in the plot are based on Central European Time.
                     

data/2024-02-06 13:25:55.133644_general_plot.csv

@@ -0,0 +1,284 @@
+path,plot_object,header,description,plot_json
+./html/plots/response_times.html,"Figure({
+    'data': [{'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'Mistral-7B-Instruct-v0.2',
+              'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
+              'name': 'Mistral-7B-Instruct-v0.2',
+              'offsetgroup': 'Mistral-7B-Instruct-v0.2',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['Mistral-7B-Instruct-v0.2'], dtype=object),
+              'xaxis': 'x',
+              'y': array([3.27102681]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'Mixtral-8x7B-Instruct-v0.1',
+              'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
+              'name': 'Mixtral-8x7B-Instruct-v0.1',
+              'offsetgroup': 'Mixtral-8x7B-Instruct-v0.1',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['Mixtral-8x7B-Instruct-v0.1'], dtype=object),
+              'xaxis': 'x',
+              'y': array([4.83500647]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+              'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
+              'name': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+              'offsetgroup': 'TinyLlama/TinyLlama-1.1B-Chat-v1.0',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['TinyLlama/TinyLlama-1.1B-Chat-v1.0'], dtype=object),
+              'xaxis': 'x',
+              'y': array([1.79273835]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'chat-bison',
+              'marker': {'color': '#ab63fa', 'pattern': {'shape': ''}},
+              'name': 'chat-bison',
+              'offsetgroup': 'chat-bison',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['chat-bison'], dtype=object),
+              'xaxis': 'x',
+              'y': array([3.89024363]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'chat-bison (PaLM 2)',
+              'marker': {'color': '#FFA15A', 'pattern': {'shape': ''}},
+              'name': 'chat-bison (PaLM 2)',
+              'offsetgroup': 'chat-bison (PaLM 2)',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['chat-bison (PaLM 2)'], dtype=object),
+              'xaxis': 'x',
+              'y': array([2.66830386]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'chat-bison-32k',
+              'marker': {'color': '#19d3f3', 'pattern': {'shape': ''}},
+              'name': 'chat-bison-32k',
+              'offsetgroup': 'chat-bison-32k',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['chat-bison-32k'], dtype=object),
+              'xaxis': 'x',
+              'y': array([4.12817777]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'chat-bison-32k (PaLM 2 32K)',
+              'marker': {'color': '#FF6692', 'pattern': {'shape': ''}},
+              'name': 'chat-bison-32k (PaLM 2 32K)',
+              'offsetgroup': 'chat-bison-32k (PaLM 2 32K)',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['chat-bison-32k (PaLM 2 32K)'], dtype=object),
+              'xaxis': 'x',
+              'y': array([6.73886213]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'gemini-pro',
+              'marker': {'color': '#B6E880', 'pattern': {'shape': ''}},
+              'name': 'gemini-pro',
+              'offsetgroup': 'gemini-pro',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['gemini-pro'], dtype=object),
+              'xaxis': 'x',
+              'y': array([3.03678889]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'gpt-3.5-turbo',
+              'marker': {'color': '#FF97FF', 'pattern': {'shape': ''}},
+              'name': 'gpt-3.5-turbo',
+              'offsetgroup': 'gpt-3.5-turbo',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['gpt-3.5-turbo'], dtype=object),
+              'xaxis': 'x',
+              'y': array([4.29307391]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'gpt-4',
+              'marker': {'color': '#FECB52', 'pattern': {'shape': ''}},
+              'name': 'gpt-4',
+              'offsetgroup': 'gpt-4',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['gpt-4'], dtype=object),
+              'xaxis': 'x',
+              'y': array([14.32373439]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'gpt-4-turbo',
+              'marker': {'color': '#636efa', 'pattern': {'shape': ''}},
+              'name': 'gpt-4-turbo',
+              'offsetgroup': 'gpt-4-turbo',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['gpt-4-turbo'], dtype=object),
+              'xaxis': 'x',
+              'y': array([15.5354884]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'llama-2-70b-chat',
+              'marker': {'color': '#EF553B', 'pattern': {'shape': ''}},
+              'name': 'llama-2-70b-chat',
+              'offsetgroup': 'llama-2-70b-chat',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['llama-2-70b-chat'], dtype=object),
+              'xaxis': 'x',
+              'y': array([3.6742315]),
+              'yaxis': 'y'},
+             {'alignmentgroup': 'True',
+              'hovertemplate': 'Model=%{x}<br>Execution time (s)=%{y}<extra></extra>',
+              'legendgroup': 'zephyr-7b-beta',
+              'marker': {'color': '#00cc96', 'pattern': {'shape': ''}},
+              'name': 'zephyr-7b-beta',
+              'offsetgroup': 'zephyr-7b-beta',
+              'orientation': 'v',
+              'showlegend': True,
+              'textposition': 'auto',
+              'type': 'bar',
+              'x': array(['zephyr-7b-beta'], dtype=object),
+              'xaxis': 'x',
+              'y': array([3.6370095]),
+              'yaxis': 'y'}],
+    'layout': {'barmode': 'relative',
+               'legend': {'title': {'text': 'Model'}, 'tracegroupgap': 0},
+               'template': '...',
+               'title': {'text': 'Model endpoints response times'},
+               'xaxis': {'anchor': 'y',
+                         'categoryarray': [Mistral-7B-Instruct-v0.2,
+                                           Mixtral-8x7B-Instruct-v0.1,
+                                           TinyLlama/TinyLlama-1.1B-Chat-v1.0,
+                                           chat-bison, chat-bison (PaLM 2), chat-
+                                           bison-32k, chat-bison-32k (PaLM 2 32K),
+                                           gemini-pro, gpt-3.5-turbo, gpt-4,
+                                           gpt-4-turbo, llama-2-70b-chat,
+                                           zephyr-7b-beta],
+                         'categoryorder': 'array',
+                         'domain': [0.0, 1.0],
+                         'title': {'text': 'Model'}},
+               'yaxis': {'anchor': 'x', 'domain': [0.0, 1.0], 'title': {'text': 'Execution time (s)'}}}
+})",Model endpoints response times,"This plot shows averaged response time for one query for each model.
+
+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. 
+
+It is also important to note that wee used litellm python library for all of the models which adds some overhead compared to pure curl calls.","{""data"":[{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time (s)=%{y}\u003cextra\u003e\u003c\u002fextra\u003e"",""legendgroup"":""Mistral-7B-Instruct-v0.2"",""marker"":{""color"":""#636efa"",""pattern"":{""shape"":""""}},""name"":""Mistral-7B-Instruct-v0.2"",""offsetgroup"":""Mistral-7B-Instruct-v0.2"",""orientation"":""v"",""showlegend"":true,""textposition"":""auto"",""x"":[""Mistral-7B-Instruct-v0.2""],""xaxis"":""x"",""y"":[3.271026806036631],""yaxis"":""y"",""type"":""bar""},{""alignmentgroup"":""True"",""hovertemplate"":""Model=%{x}\u003cbr\u003eExecution time 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+               'title': {'text': 'Costs of execution of 800 test queries per model'},
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