testing lftk

app.py

@@ -1,7 +1,7 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import lftk
-from lftk import LFTK
+import spacy
 import time
 import os
 import openai
@@ -16,14 +16,27 @@ llama_model = AutoModelForCausalLM.from_pretrained("daryl149/llama-2-7b-chat-hf"
 
 template_single = '''Please output any <{}> in the following sentence one per line without any additional text: "{}"'''
 
-lftk_instance = LFTK()
+def linguistic_features(message):
+    # Load a trained spaCy pipeline
+    nlp = spacy.load("en_core_web_sm")
 
-def linguistic_features(linguistic_features, message):
-    # Use LFTK to extract linguistic features
-    linguistic_features = lftk_instance.extract_features(message)
-    print('Vicuna Linguistic Features:', linguistic_features)
+    # Create a spaCy doc object
+    doc = nlp(message)
+
+    # Initiate LFTK extractor by passing in the doc
+    LFTK_extractor = lftk.Extractor(docs=doc)
+
+    # Customize LFTK extractor (optional)
+    LFTK_extractor.customize(stop_words=True, punctuations=False, round_decimal=3)
+
+    # Use LFTK to dynamically extract handcrafted linguistic features
+    features_to_extract = lftk.search_features(family="wordsent", language="general", return_format="list_key")
+    extracted_features = LFTK_extractor.extract(features=features_to_extract)
+
+    print('Linguistic Features:', extracted_features)
+
+    return extracted_features
 
-    return linguistic_features
 
 def update_api_key(new_key):
     global api_key
@@ -138,8 +151,8 @@ def interface():
         #textbox_prompt.submit(llama_respond, inputs=[textbox_prompt, llama_S1_chatbot], outputs=[textbox_prompt, llama_S1_chatbot])
 
         btn.click(lambda _, 
-                  message=textbox_prompt: linguistic_features_textbox.update(linguistic_features(linguistic_features_textbox, message)), 
-                  inputs=[tab_name, textbox_prompt], 
+                  message=textbox_prompt: linguistic_features_textbox.update(linguistic_features(textbox_prompt)), 
+                  inputs=[textbox_prompt], 
                   outputs=[linguistic_features_textbox])
         
         btn.click(vicuna_respond, inputs=[tab_name, textbox_prompt, vicuna_S1_chatbot],