try wiring up feedback element

app.py

@@ -1,10 +1,14 @@
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoModelForSeq2SeqLM
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-large")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-large")
+grammar_tokenizer = AutoTokenizer.from_pretrained("prithivida/grammar_error_correcter_v1")
+grammar_model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/grammar_error_correcter_v1")
 import torch
 import gradio as gr
 
 
 def chat(message, history):
-    history = history or [('hi', 'hello'), ('what ya doing', 'nothing')]
+    history = history or []
     if message.startswith("How many"):
         response = random.randint(1, 10)
     elif message.startswith("How"):
@@ -14,13 +18,40 @@ def chat(message, history):
     else:
         response = "I don't know"
     history.append((message, response))
-    return history, history
+    return history, feedback(message)
+
+
+def feedback(text):
+    tokenized_phrases = grammar_tokenizer([text], return_tensors='pt', padding=True)
+    corrections = grammar_model.generate(**tokenized_phrases)
+    corrections = grammar_tokenizer.batch_decode(corrections, skip_special_tokens=True)
+    print("The corrections are: ", corrections)
+    if corrections[0] == text:
+        feedback = f'Looks good! Keep up the good work'
+    else:
+        feedback = f'\'{corrections[0]}\' might be a little better'
+    return f'FEEDBACK:  {feedback}'
 
 iface = gr.Interface(
     chat,
     ["text", "state"],
-    ["chatbot", "state"],
+    ["chatbot", "text"],
     allow_screenshot=False,
     allow_flagging="never",
 )
 iface.launch()
+
+
+
+new_user_input_ids = tokenizer.encode(text+tokenizer.eos_token, return_tensors='pt')
+    # append the new user input tokens to the chat history
+    bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if chat_history_ids is not None else new_user_input_ids
+
+    # generated a response while limiting the total chat history to 1000 tokens, 
+    chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id)
+    print("The text is ", [text])
+
+    # pretty print last ouput tokens from bot
+    output =  tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    print("The outout is :", output)
+    text_session.append(output)