ok

app.py

@@ -2,15 +2,19 @@ import gradio as gr
 from langchain.chat_models import ChatOpenAI
 from langchain.chains import ConversationChain  
 from transformers import pipeline
+from transformers import AutoTokenizer, AutoModelWithLMHead
+
 
 model_name="nateraw/bert-base-uncased-emotion"
 model = pipeline('text-classification', model_name, truncation=True)
 
-from transformers import AutoTokenizer, AutoModelWithLMHead
 model_name = "mrm8488/t5-base-finetuned-emotion"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model_t5 = AutoModelWithLMHead.from_pretrained(model_name)
 
+model_path = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
+sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path)
+
 def get_emotion(text):
   input_ids = tokenizer.encode(text + '</s>', return_tensors='pt')
   output = model_t5.generate(input_ids=input_ids, return_dict_in_generate=True, output_scores=True)
@@ -37,7 +41,10 @@ with gr.Blocks() as demo:
         label_value = f"{l['label']} [{l['score']}]"
         label_value_t5 = get_emotion(bot_message)
         
-        return "", chat_history, f"Model1: {label_value_t5} - Model2: {label_value}"
+        s = sentiment_task(bot_message)
+        sentiment_value = f"{s['label']} [{s['score']}]"
+        
+        return "", chat_history, f"Emotion [1]: {label_value_t5} - Emotion [2]: {label_value} - Sentiment : {sentiment_value}"
 
     msg.submit(respond, [msg, chatbot], [msg, chatbot, label_text])
 

requirements.txt

@@ -1,5 +1,5 @@
-langchain
-openai 
-transformers
-torch
+langchain
+openai 
+transformers
+torch
 sentencepiece