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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)
st.title("Simple Streamlit Chatbot")
# User input text box
user_input = st.text_input("You: ", key="user_input")
# Button to send the message
if st.button("Send"):
# Generating a response
response = get_response(user_input)
# Displaying the conversation
# Here, we use st.session_state to keep track of the conversation
if 'conversation' not in st.session_state:
st.session_state.conversation = []
# Append the user input and bot response to the conversation
st.session_state.conversation.append("You: " + user_input)
st.session_state.conversation.append("Bot: " + response)
# Display each line in the conversation
for line in st.session_state.conversation:
st.text(line)
#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)) |