Add application file

app.py

@@ -0,0 +1,118 @@
+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()