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main.py

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+import transformers
+from transformers import TFGPT2LMHeadModel ,GPT2Tokenizer, BitsAndBytesConfig
+import tensorflow as tf
+tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
+model = TFGPT2LMHeadModel.from_pretrained('gpt2',pad_token_id = tokenizer.eos_token_id)
+def generate_text(inp):
+  input_ids = tokenizer.encode(inp,return_tensors = 'tf')
+  beam_output =  model.generate(input_ids, max_length = 100,num_beams = 5, no_repeat_ngram_size = 2, early_stopping = True)
+  output = tokenizer.decode(beam_output[0],skip_special_tokens = True, clean_up_tokenization_spaces = True)
+  return ".".join(output.split(".")[:-1]) + "."

stream.py

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+import streamlit as st 
+from main import generate_text
+st.title("Health Suggestion Bot - Prakruti 🍃")
+st.write("Prakruti, in Ayurveda, refers to an individual's unique physical and psychological constitution. It is determined by the balance of three fundamental energies or doshas: Vata, Pitta, and Kapha. Understanding one's Prakruti helps tailor health and lifestyle choices for overall well-being.")
+if "messages" not in st.session_state:
+    st.session_state.messages = []
+    st.session_state.messages.append({
+        'role':'assistant',
+        'content':"Hi! I'm your virtual assistant you can ask any query to me"
+    })
+for message in st.session_state.messages:
+  with st.chat_message(message["role"]):
+    st.markdown(message["content"])
+prompt = st.chat_input("What is up?")
+if prompt:
+  with st.chat_message("user"):
+    st.markdown(prompt)
+  st.session_state.messages.append({"role":"user","content":prompt})
+  response = f"ChatBot: {generate_text(prompt)}"
+  with st.chat_message("assistant"):
+    st.markdown(response)
+  st.session_state.messages.append({"role":"assistant","content":response})