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import streamlit as st
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import time
# Load the fine-tuned model
model_path = r"C:\Users\Kush\Desktop\dfuchatbot\nlp"
tokenizer = GPT2Tokenizer.from_pretrained(model_path)
model = GPT2LMHeadModel.from_pretrained(model_path)
# Set page title and favicon
st.set_page_config(page_title="Diabetic Foot Ulcer Chatbot", page_icon=":hospital:")
# Set app title and description
st.title("Diabetic Foot Ulcer Chatbot")
st.markdown("Welcome to the Diabetic Foot Ulcer Chatbot. Ask any questions related to diabetic foot ulcers!")
# Define conversation loop
def chatbot(user_input):
# Check for exit command
if user_input.lower() == 'exit':
st.info("Chat ended. Goodbye!")
return
# Display typing animation
with st.spinner(text="Chatbot is typing..."):
time.sleep(2) # Simulate typing time
# Get model response
response = generate_response(user_input)
# Display model response
st.text_area("Chatbot:", value=response, height=100, max_chars=500)
# Generate response from the model
def generate_response(user_input):
# Encode user input
input_ids = tokenizer.encode(user_input, return_tensors="pt")
# Generate model response
output = model.generate(
input_ids,
max_length=100,
num_return_sequences=1,
no_repeat_ngram_size=2,
top_k=50,
top_p=0.95,
temperature=0.7,
do_sample=True,
pad_token_id=tokenizer.eos_token_id,
early_stopping=True
)
# Decode and return model response
return tokenizer.decode(output[0], skip_special_tokens=True)
# Get user input
user_input = st.text_input("You:")
# Start conversation when user submits input
if st.button("Send"):
chatbot(user_input)
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