Create app.py

app.py

@@ -0,0 +1,43 @@
+# https://ai-brewery.medium.com/conversational-chatbot-using-transformers-and-streamlit-73d621afde9
+import streamlit as st
+import torch
+import transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+@st.cache(hash_funcs=
+    {transformers.models.gpt2.tokenization_gpt2_fast.GPT2TokenizerFast: hash}, 
+    suppress_st_warning=True)
+
+def load_data():    
+    tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+    model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+    return tokenizer, model
+
+tokenizer, model = load_data()
+
+st.write("Welcome to the Chatbot. I am still learning, please be patient")
+input = st.text_input('User:')
+if 'count' not in st.session_state or st.session_state.count == 6:
+    st.session_state.count = 0 
+    st.session_state.chat_history_ids = None
+    st.session_state.old_response = ''
+else:
+    st.session_state.count += 1
+
+
+new_user_input_ids = tokenizer.encode(input + tokenizer.eos_token, return_tensors='pt')
+
+bot_input_ids = torch.cat([st.session_state.chat_history_ids, new_user_input_ids], dim=-1) if st.session_state.count > 1 else new_user_input_ids
+
+st.session_state.chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id)
+
+response = tokenizer.decode(st.session_state.chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+if st.session_state.old_response == response:
+    bot_input_ids = new_user_input_ids
+    
+    st.session_state.chat_history_ids = model.generate(bot_input_ids, max_length=5000, pad_token_id=tokenizer.eos_token_id)
+    response = tokenizer.decode(st.session_state.chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+
+st.write(f"Chatbot: {response}")
+st.session_state.old_response = response