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| import streamlit as st | |
| from streamlit_chat import message as st_message | |
| from transformers import BlenderbotTokenizer | |
| from transformers import BlenderbotForConditionalGeneration | |
| def get_models(): | |
| # pytorch keeps an internal state of the conversation | |
| model_name = "facebook/blenderbot-400M-distill" | |
| tokenizer = BlenderbotTokenizer.from_pretrained(model_name) | |
| model = BlenderbotForConditionalGeneration.from_pretrained(model_name) | |
| return tokenizer, model | |
| if "history" not in st.session_state: | |
| st.session_state.history = [] | |
| st.title("Hello Chatbot") | |
| def generate_answer(): | |
| tokenizer, model = get_models() | |
| user_message = st.session_state.input_text | |
| inputs = tokenizer(st.session_state.input_text, return_tensors="pt") | |
| result = model.generate(**inputs) | |
| message_bot = tokenizer.decode( | |
| result[0], skip_special_tokens=True | |
| ) # .replace("<s>", "").replace("</s>", "") | |
| st.session_state.history.append({"message": user_message, "is_user": True}) | |
| st.session_state.history.append({"message": message_bot, "is_user": False}) | |
| st.text_input("Talk to the bot", key="input_text", on_change=generate_answer) | |
| for chat in st.session_state.history: | |
| st_message(**chat) # unpacking |