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from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import streamlit as st
from peft import PeftModel, PeftConfig
tokenizer = AutoTokenizer.from_pretrained("SSahas/openai_community_med_e3")
#model = AutoModelForCausalLM.from_pretrained("SSahas/openai_community_med_e3")
config = PeftConfig.from_pretrained("SSahas/openai_community_med_e3")
model = AutoModelForCausalLM.from_pretrained("openai-community/gpt2-medium")
model = PeftModel.from_pretrained(model, "SSahas/openai_community_med_e3")
def response_generator(prompt):
input_text = tokenizer.apply_chat_template(
prompt, tokenize=False, truncation=False, add_generation_prompt=True)
print(input_text)
input_ids = tokenizer(input_text, padding=True, return_tensors="pt")
output_ids = model.generate(input_ids=input_ids['input_ids'], generation_config=GenerationConfig(
max_new_tokens=30, pad_token_id=50256))
output = tokenizer.decode(
output_ids[0][input_ids['input_ids'].shape[1]:], skip_special_tokens=True)
return output
st.title("Simple friendly chatbot for normal conversations")
# Initialize chat history
if "messages" not in st.session_state:
st.session_state.messages = []
# Display chat messages from history on app rerun
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Accept user input
if prompt := st.chat_input("What is up?"):
# Add user message to chat history
st.session_state.messages.append({"role": "user", "content": prompt})
# Display user message in chat message container
with st.chat_message("user"):
st.markdown(prompt)
# Display assistant response in chat message container
with st.chat_message("assistant"):
# response = st.write(response_generator(prompt))
# print(prompt)
print(st.session_state.messages)
response = response_generator(st.session_state.messages)
st.write(response)
# Add assistant response to chat history
st.session_state.messages.append(
{"role": "assistant", "content": response})
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