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import streamlit as st
from streamlit_chat import message
from transformers import AutoTokenizer, AutoModelForCausalLM
# Initialize the model_name key in the st.session_state dictionary
if 'model_name' not in st.session_state:
st.session_state['model_name'] = "microsoft/DialoGPT-medium"
# Define the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(st.session_state['model_name'])
model = AutoModelForCausalLM.from_pretrained(st.session_state['model_name'])
if 'conversation' not in st.session_state:
st.session_state['conversation'] = None
if 'messages' not in st.session_state:
st.session_state['messages'] = []
# Setting page title and header
st.set_page_config(page_title="Chat GPT Clone", page_icon=":robot_face:")
st.markdown("<h1 style='text-align: center;'>💻 ChatterBot</h1>", unsafe_allow_html=True)
st.subheader("How Can I Help You Today? 🤖")
st.sidebar.title("😎🗝️")
st.session_state['model_name'] = st.sidebar.text_input("Hugging Face Model Name", value="microsoft/DialoGPT-medium")
summarise_button = st.sidebar.button("Summarise the conversation", key="summarise")
st.sidebar.image('./chatbot.jpg', width=300, use_column_width=True)
if summarise_button:
summarise_placeholder = st.sidebar.write("Nice chatting with you my friend ❤️:\n\n" + st.session_state['conversation'])
def get_response(userInput):
if st.session_state['conversation'] is None:
st.session_state['conversation'] = ""
input_ids = tokenizer.encode(userInput + tokenizer.eos_token, return_tensors="pt")
outputs = model.generate(input_ids, max_length=1024)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
st.session_state['conversation'] += "\n" + response
return response
response_container = st.container()
# Here we will have a container for user input text box
container = st.container()
with container:
with st.form(key='my_form', clear_on_submit=True):
user_input = st.text_area("Your question goes here:", key='input', height=100)
submit_button = st.form_submit_button(label='Send')
if submit_button:
st.session_state['messages'].append(user_input)
model_response = get_response(user_input)
st.session_state['messages'].append(model_response)
with response_container:
for i in range(len(st.session_state['messages'])):
if (i % 2) == 0:
message(st.session_state['messages'][i], is_user=True, key=str(i) + '_user')
else:
message(st.session_state['messages'][i], key=str(i) + '_AI') |