Spaces:
Sleeping
Sleeping
File size: 2,850 Bytes
64c6e7a a85678b a2142e5 a85678b 64c6e7a a85678b 93e9f31 a85678b 93e9f31 a85678b 93e9f31 10bdb16 a85678b 93e9f31 10bdb16 93e9f31 10bdb16 6fd5a3c 93e9f31 10bdb16 a85678b 93e9f31 10bdb16 a85678b bdedfa4 64c6e7a a85678b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 |
import streamlit as st
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Function to generate a response
def generate_response(input_text):
# Adjusted input to include the [Bot] marker
#adjusted_input = f"{input_text} [Bot]"
# Encode the adjusted input
inputs = tokenizer(input_text, return_tensors="pt")
# Generate a sequence of text with a slightly increased max_length to account for the prompt length
output_sequences = model.generate(
input_ids=inputs['input_ids'],
attention_mask=inputs['attention_mask'],
max_length=100, # Adjusted max_length
temperature=0.7,
top_k=50,
top_p=0.95,
no_repeat_ngram_size=2,
pad_token_id=tokenizer.eos_token_id,
#early_stopping=True,
do_sample=True
)
# Decode the generated sequence
full_generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)
# Extract the generated response after the [Bot] marker
bot_response_start = full_generated_text.find('[Bot]') + len('[Bot]')
bot_response = full_generated_text[bot_response_start:]
# Trim the response to end at the last period within the specified max_length
last_period_index = bot_response.rfind('.')
if last_period_index != -1:
bot_response = bot_response[:last_period_index + 1]
return bot_response.strip()
# Load pre-trained model tokenizer (vocabulary) and model
model_name = 'KhantKyaw/Chat_GPT-2'
tokenizer = GPT2Tokenizer.from_pretrained(model_name)
model = GPT2LMHeadModel.from_pretrained(model_name)
# Chat loop
#print("Chatbot is ready. Type 'quit' to exit.")
#while True:
#user_input = input("You: ")
#if user_input.lower() == "quit":
#break
#response = generate_response(user_input)
#print("Chatbot:", response)
# Set the title of the web app
st.title("Simple Chatbot")
# Initialize session state to store chat history
if 'chat_history' not in st.session_state:
st.session_state.chat_history = []
# Chat input
user_input = st.chat_input(placeholder="Say Something!", key="chat_input")
# Check if there is an input
if user_input:
# Generate a response based on the user input
response = generate_response(user_input)
# Update chat history with the user input and bot response
st.session_state.chat_history.append(("You", user_input))
st.session_state.chat_history.append(("Bot", response))
# Display chat history
for author, text in st.session_state.chat_history:
st.chat_message(text=text, author=author)
#prompt = st.chat_input(placeholder="Say Something!",key=None, max_chars=None, disabled=False, on_submit=None, args=None, kwargs=None)
#if prompt:
# with st.chat_message(name="AI",avatar=None):
# st.write(generate_response(prompt)) |