import pandas as pd import plotly.express as px from src.assets.text_content import SHORT_NAMES def plotly_plot(df:pd.DataFrame, LIST:list, ALL:list, NAMES:list, LEGEND:list): ''' Takes in a list of models for a plotly plot Args: df: A dummy dataframe of latest version LIST: List of models to plot ALL: Either [] or ["Show All Models"] - toggle view to plot all models NAMES: Either [] or ["Show Names"] - toggle view to show model names on plot LEGEND: Either [] or ["Show Legend"] - toggle view to show legend on plot Returns: Fig: plotly figure ''' # Get list of all models and append short names column to df list_columns = list(df.columns) ALL_LIST = list(df[list_columns[0]].unique()) short_names = label_map(ALL_LIST) list_short_names = list(short_names.values()) df["Short"] = list_short_names if ALL: LIST = ALL_LIST # Filter dataframe based on the provided list of models df = df[df[list_columns[0]].isin(LIST)] if NAMES: fig = px.scatter(df, x=list_columns[2], y=list_columns[3], color=list_columns[0], symbol=list_columns[0], color_discrete_map={"category1": "blue", "category2": "red"}, hover_name=list_columns[0], template="plotly_white", text="Short") fig.update_traces(textposition='top center') else: fig = px.scatter(df, x=list_columns[2], y=list_columns[3], color=list_columns[0], symbol=list_columns[0], color_discrete_map={"category1": "blue", "category2": "red"}, hover_name=list_columns[0], template="plotly_white") if not LEGEND: fig.update_layout(showlegend=False) fig.update_layout( xaxis_title='% Played', yaxis_title='Quality Score', title='Overview of benchmark results', height=1000 ) fig.update_xaxes(range=[-5, 105]) fig.update_yaxes(range=[-5, 105]) return fig # ['Model', 'Clemscore', 'All(Played)', 'All(Quality Score)'] def compare_plots(df: pd.DataFrame, LIST1: list, LIST2: list, ALL:list, NAMES:list, LEGEND: list): ''' Quality Score v/s % Played plot by selecting models Args: df: A dummy dataframe of latest version LIST1: The list of open source models to show in the plot, updated from frontend LIST2: The list of commercial models to show in the plot, updated from frontend ALL: Either [] or ["Show All Models"] - toggle view to plot all models NAMES: Either [] or ["Show Names"] - toggle view to show model names on plot LEGEND: Either [] or ["Show Legend"] - toggle view to show legend on plot Returns: fig: The plot ''' # Combine lists for Open source and commercial models LIST = LIST1 + LIST2 fig = plotly_plot(df, LIST, ALL, NAMES, LEGEND) return fig def shorten_model_name(full_name): # Split the name into parts parts = full_name.split('-') # Process the name parts to keep only the parts with digits (model sizes and versions) short_name_parts = [part for part in parts if any(char.isdigit() for char in part)] if len(parts) == 1: short_name = ''.join(full_name[0:min(3, len(full_name))]) else: # Join the parts to form the short name short_name = '-'.join(short_name_parts) # Remove any leading or trailing hyphens short_name = full_name[0] + '-'+ short_name.strip('-') return short_name def label_map(model_list: list) -> dict: ''' Generate a map from long names to short names, to plot them in frontend graph Define the short names in src/assets/text_content.py Args: model_list: A list of long model names Returns: short_name: A dict from long to short name ''' short_names = {} for model_name in model_list: if model_name in SHORT_NAMES: short_name = SHORT_NAMES[model_name] else: short_name = shorten_model_name(model_name) # Define the short name and indicate both models are same short_names[model_name] = short_name return short_names def split_models(MODEL_LIST: list): ''' Split the models into open source and commercial ''' open_models = [] comm_models = [] for model in MODEL_LIST: if model.startswith(('gpt-', 'claude-', 'command')): comm_models.append(model) else: open_models.append(model) open_models.sort(key=lambda o: o.upper()) comm_models.sort(key=lambda c: c.upper()) return open_models, comm_models