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| import streamlit as st | |
| import seaborn as sns | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| class LLaMAScoreAnalyzer: | |
| def __init__(self): | |
| self.languages = ["Nepali", "Hindi"] | |
| self.models = ["Baseline", "LoRA"] | |
| self.scores_gpt = ["relevance_score", "cc_score", "syntax_score", "complete_score"] | |
| self.rouge_bleu = ["rougeL", "bleu"] | |
| self.categories = ["hallucination_type"] | |
| self.DATA_PATH = { | |
| "Nepali": {"Baseline": "./data/nepali_baseline_all_scores.csv", "LoRA": "./data/nepali_lora_all_scores.csv"}, | |
| "Hindi": {"Baseline": "./data/hindi_baseline_all_scores.csv", "LoRA": "./data/hindi_lora_all_scores.csv"} | |
| } | |
| def load_samples(self, lang): | |
| cols_to_show = ["instruction", "input", "output"] | |
| for model in self.DATA_PATH[lang]: | |
| df = pd.read_csv(self.DATA_PATH[lang][model]) | |
| df.rename({"output": "expected_output"}) | |
| df[model+"_Response"] = df["cleaned_response"] | |
| cols_to_show.append(model+"_Response") | |
| cols_to_show = cols_to_show + ["relevance_score", "cc_score", "syntax_score", "complete_score", "rougeL", "blue", "is_repeat", "hallucination_type"] | |
| df = df[[col for col in cols_to_show if col in df.columns]] | |
| st.write(df.sample(5)) | |
| def load_data(self, lang, model): | |
| df = pd.read_csv(self.DATA_PATH[lang][model]) | |
| df['Language'] = lang | |
| df['Model'] = model | |
| return df | |
| def draw_specific_plots(self, data, categories, x_variable, title): | |
| fig, ax = plt.subplots(figsize=(12, 6)) | |
| palette = sns.color_palette("pastel", len(categories) * len(data[x_variable].unique())) | |
| for i, category in enumerate(categories): | |
| for j, unique_value in enumerate(data[x_variable].unique()): | |
| subset = data[data[x_variable] == unique_value] | |
| sns.kdeplot(data=subset, x=category, fill=True, common_norm=False, alpha=0.5, | |
| ax=ax, color=palette[i * len(data[x_variable].unique()) + j], | |
| label=f"{category} ({unique_value})") | |
| ax.set_title(title, fontsize=16) | |
| ax.set_xlabel("Score", fontsize=12) | |
| ax.set_ylabel("Density", fontsize=12) | |
| ax.legend(title="Category (Language/Model)") | |
| return fig | |
| def draw_combined_density_plot(self, data, title): | |
| fig, ax = plt.subplots(figsize=(12, 8)) | |
| palette = sns.color_palette("pastel", len(self.scores_gpt)) | |
| for i, category in enumerate(self.scores_gpt): | |
| sns.kdeplot(data=data, x=category, fill=True, common_norm=False, alpha=0.5, ax=ax, label=category, color=palette[i]) | |
| ax.set_title(title, fontsize=16) | |
| ax.set_xlabel("Score", fontsize=12) | |
| ax.set_ylabel("Density", fontsize=12) | |
| ax.legend(title="Score Categories") | |
| return fig | |
| def draw_bar_plot(self, data, categories, x_variable, title): | |
| fig, axs = plt.subplots(len(categories), 1, figsize=(10, 6 * len(categories))) | |
| palette = sns.color_palette("pastel", len(data)) | |
| if len(categories) == 1: | |
| axs = [axs] # Ensure axs is iterable even for a single plot | |
| for i, category in enumerate(categories): | |
| sns.countplot(data=data, x=category, hue=x_variable, palette=palette, ax=axs[i]) | |
| axs[i].set_title(f"Distribution of {category} for {title}", fontsize=16) | |
| axs[i].set_xlabel(category, fontsize=12) | |
| axs[i].set_ylabel("Count", fontsize=12) | |
| axs[i].legend(title=x_variable) | |
| plt.tight_layout() | |
| return fig | |
| def score_analyzer(self): | |
| st.sidebar.markdown(""" | |
| This App was created as a part of the project: "Fine-tuning LLaMA 3 with Low-Rank Adaptation for Nepali and Hindi" | |
| """) | |
| st.title("Findings from Fine-tuning LLaMA 3 with Low-Rank Adaptation for Nepali and Hind! ") | |
| st.markdown(""" | |
| Full post here: | |
| """) | |
| show_samples = st.sidebar.checkbox("Show Sample Data", value=False) | |
| detailed_view = st.sidebar.checkbox("Enable Detailed Charts View", value=False) | |
| selected_languages = st.sidebar.multiselect("Select Languages", self.languages, default="Nepali") | |
| selected_gpt_scoring = st.sidebar.multiselect("Select Score Category", self.scores_gpt, default="relevance_score") | |
| selected_models = st.sidebar.multiselect("Select Models", self.models, default="Baseline") | |
| dfs = [] | |
| for lang in selected_languages: | |
| for model in selected_models: | |
| df = self.load_data(lang, model) | |
| dfs.append(df) | |
| if show_samples: | |
| for lang in selected_languages: | |
| st.write(f"Sample data for {lang}") | |
| self.load_samples(lang) | |
| combined_data = pd.concat(dfs, ignore_index=True) | |
| if detailed_view: | |
| for language in selected_languages: | |
| language_data = combined_data[combined_data['Language'] == language] | |
| title = f"Distribution of Scores for {language}" | |
| fig = self.draw_specific_plots(language_data, selected_gpt_scoring, 'Model', title) | |
| st.pyplot(fig) | |
| if len(selected_languages) > 1: | |
| for model in selected_models: | |
| model_data = combined_data[combined_data['Model'] == model] | |
| title = f"Distribution of Scores for {model}" | |
| fig = self.draw_specific_plots(model_data, selected_gpt_scoring, 'Language', title) | |
| st.pyplot(fig) | |
| st.sidebar.markdown(""" | |
| Show additional evaluation scores and categories below: | |
| """) | |
| additional_score_categories = st.sidebar.checkbox("Hallucination Statistics", value=False) | |
| if additional_score_categories: | |
| additional_categories = st.sidebar.multiselect("Select Category", self.categories, default="hallucination_type") | |
| for language in selected_languages: | |
| language_data = combined_data[combined_data['Language'] == language] | |
| title = f"{language}" | |
| fig = self.draw_bar_plot(language_data, additional_categories, 'Model', title) | |
| st.pyplot(fig) | |
| if len(selected_languages) > 1: | |
| for model in selected_models: | |
| model_data = combined_data[combined_data['Model'] == model] | |
| title = f"{model}" | |
| fig = self.draw_bar_plot(model_data, additional_categories, 'Language', title) | |
| st.pyplot(fig) | |
| else: | |
| for language in selected_languages: | |
| for model in selected_models: | |
| title = f"Distribution of Scores for Different Evaluation Criterias for {language} [{model} Model]" | |
| fig = self.draw_combined_density_plot(combined_data[(combined_data['Language'] == language) & | |
| (combined_data['Model'] == model)], title) | |
| st.pyplot(fig) | |
| def main(): | |
| st.sidebar.header("Findings from Fine-tuning LLaMA 3 with Low-Rank Adaptation for Nepali and Hindi!") | |
| analyzer = LLaMAScoreAnalyzer() | |
| analyzer.score_analyzer() | |
| if __name__ == "__main__": | |
| main() | |