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import gradio as gr
from theme_classifier import ThemeClassifier
from character_network import NamedEntityRecognizer, CharacterNetworkGenerator
from text_classification import JutsuClassifier
from character_chatbot import CharacterChatBot
import os
from dotenv import load_dotenv
load_dotenv()
def get_themes(theme_list_str,subtitles_path,save_path):
theme_list = theme_list_str.split(',')
theme_classifier = ThemeClassifier(theme_list)
output_df = theme_classifier.get_themes(subtitles_path,save_path)
# Remove dialogue from the theme list
theme_list = [theme for theme in theme_list if theme != 'dialogue']
output_df = output_df[theme_list]
output_df = output_df[theme_list].sum().reset_index()
output_df.columns = ['Theme','Score']
output_chart = gr.BarPlot(
output_df,
x="Theme",
y="Score",
title="Series Themes",
tooltip=["Theme","Score"],
vertical=False,
width=500,
height=260
)
return output_chart
def get_character_network(subtitles_path,ner_path):
ner = NamedEntityRecognizer()
ner_df = ner.get_ners(subtitles_path,ner_path)
character_network_generator = CharacterNetworkGenerator()
relationship_df = character_network_generator.generate_character_network(ner_df)
html = character_network_generator.draw_network_graph(relationship_df)
return html
def classify_text(text_classifcation_model,text_classifcation_data_path,text_to_classify):
jutsu_classifier = JutsuClassifier(model_path = text_classifcation_model,
data_path = text_classifcation_data_path,
huggingface_token = os.getenv('huggingface_token'))
output = jutsu_classifier.classify_jutsu(text_to_classify)
output = output[0]
return output
def chat_with_character_chatbot(message, history):
character_chatbot = CharacterChatBot("AbdullahTarek/Naruto_Llama-3-8B",
huggingface_token = os.getenv('huggingface_token')
)
output = character_chatbot.chat(message, history)
output = output['content'].strip()
return output
def main():
with gr.Blocks() as iface:
# Theme Classification Section
with gr.Row():
with gr.Column():
gr.HTML("<h1>Theme Classification (Zero Shot Claasifiers)</h1>")
with gr.Row():
with gr.Column():
plot = gr.BarPlot()
with gr.Column():
theme_list = gr.Textbox(label="Themes")
subtitles_path = gr.Textbox(label="Subtitles or script Path")
save_path = gr.Textbox(label="Save Path")
get_themes_button =gr.Button("Get Themes")
get_themes_button.click(get_themes, inputs=[theme_list,subtitles_path,save_path], outputs=[plot])
# Character Network Section
with gr.Row():
with gr.Column():
gr.HTML("<h1>Character Network (NERs and Graphs)</h1>")
with gr.Row():
with gr.Column():
network_html = gr.HTML()
with gr.Column():
subtitles_path = gr.Textbox(label="Subtutles or Script Path")
ner_path = gr.Textbox(label="NERs save path")
get_network_graph_button = gr.Button("Get Character Network")
get_network_graph_button.click(get_character_network, inputs=[subtitles_path,ner_path], outputs=[network_html])
# Text Classification with LLMs
with gr.Row():
with gr.Column():
gr.HTML("<h1>Text Classification with LLMs</h1>")
with gr.Row():
with gr.Column():
text_classification_output = gr.Textbox(label="Text Classification Output")
with gr.Column():
text_classifcation_model = gr.Textbox(label='Model Path')
text_classifcation_data_path = gr.Textbox(label='Data Path')
text_to_classify = gr.Textbox(label='Text input')
classify_text_button = gr.Button("Clasify Text (Jutsu)")
classify_text_button.click(classify_text, inputs=[text_classifcation_model,text_classifcation_data_path,text_to_classify], outputs=[text_classification_output])
# Character Chatbot Section
with gr.Row():
with gr.Column():
gr.HTML("<h1>Character Chatbot</h1>")
gr.ChatInterface(chat_with_character_chatbot)
iface.launch(share=True)
if __name__ == '__main__':
main()
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