added lftk to requests and app

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
 from transformers import AutoTokenizer, AutoModelForCausalLM
+from lftk import LFTK
 import time
 import os
 import openai
@@ -14,6 +15,15 @@ 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(linguistic_features, message):
+    # Use LFTK to extract linguistic features
+    linguistic_features = lftk_instance.extract_features(message)
+    print('Vicuna Linguistic Features:', linguistic_features)
+
+    return linguistic_features
+
 def update_api_key(new_key):
     global api_key
     os.environ['OPENAI_API_TOKEN'] = new_key
@@ -55,14 +65,12 @@ def gpt_respond(tab_name, message, chat_history, max_convo_length = 10):
     chat_history.append((message, bot_message))
     return "", chat_history
 
-def vicuna_respond(tab_name, message, chat_history):
+def vicuna_respond(tab_name, message, chat_history, linguistic_features):
     formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
     print('Vicuna - Prompt + Context:')
     print(formatted_prompt)
     input_ids = vicuna_tokenizer.encode(formatted_prompt, return_tensors="pt")
-    print(input_ids)
     output_ids = vicuna_model.generate(input_ids, do_sample=True, max_length=1024, num_beams=5, no_repeat_ngram_size=2)
-    print(output_ids)
     bot_message = vicuna_tokenizer.decode(output_ids[0], skip_special_tokens=True)
     print(bot_message)
     # Remove formatted prompt from bot_message
@@ -73,7 +81,7 @@ def vicuna_respond(tab_name, message, chat_history):
     time.sleep(2)
     return tab_name, "", chat_history
 
-def llama_respond(tab_name, message, chat_history):
+def llama_respond(tab_name, message, chat_history, linguistic_features):
     formatted_prompt = f'''Generate the output only for the assistant. Please output any {tab_name} in the following sentence one per line without any additional text: {message}'''
     print('Llama - Prompt + Context:')
     print(formatted_prompt)
@@ -101,6 +109,9 @@ def interface():
         # prompt = template_single.format(tab_name, textbox_prompt)
 
         gr.Markdown("Strategy 1 QA-Based Prompting")
+
+        linguistic_features_textbox = gr.Textbox(label="Linguistic Features", disabled=True)
+
         with gr.Row():
             vicuna_S1_chatbot = gr.Chatbot(label="vicuna-7b")
             llama_S1_chatbot = gr.Chatbot(label="llama-7b")
@@ -125,8 +136,16 @@ def interface():
 
         #textbox_prompt.submit(llama_respond, inputs=[textbox_prompt, llama_S1_chatbot], outputs=[textbox_prompt, llama_S1_chatbot])
 
-        btn.click(vicuna_respond, inputs=[tab_name, textbox_prompt, vicuna_S1_chatbot], outputs=[tab_name, textbox_prompt, vicuna_S1_chatbot])
-        btn.click(llama_respond, inputs=[tab_name, textbox_prompt, llama_S1_chatbot], outputs=[tab_name, 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], 
+                  outputs=[linguistic_features_textbox])
+        
+        btn.click(vicuna_respond, inputs=[tab_name, textbox_prompt, vicuna_S1_chatbot], 
+                  outputs=[tab_name, textbox_prompt, vicuna_S1_chatbot])
+        
+        btn.click(llama_respond, inputs=[tab_name, textbox_prompt, llama_S1_chatbot], 
+                  outputs=[tab_name, textbox_prompt, llama_S1_chatbot])
 
         #api_key_btn.click(update_api_key, inputs=api_key_input)
         #btn.click(gpt_respond, inputs=[tab_name, textbox_prompt, gpt_S1_chatbot], outputs=[tab_name, textbox_prompt, gpt_S1_chatbot])

requirements.txt

@@ -5,4 +5,5 @@ sentencepiece
 fschat
 accelerate
 gpt2
-nltk
+nltk
+lftk