Create app.py

app.py

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+import streamlit as st 
+from main import generate_text
+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]) + "."
+    
+st.title("AI Chaperone 🎗️")
+st.write("AI Chaperone is an advanced AI assistant meticulously designed to provide unwavering support to individuals living with Alzheimer's disease.")
+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})