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import sentencepiece
import gradio as gr
import re
import torch
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("ahmed792002/Finetuning_T5_HealthCare_Chatbot")
model = T5ForConditionalGeneration.from_pretrained("ahmed792002/Finetuning_T5_HealthCare_Chatbot")
def clean_text(text):
text = re.sub(r'\r\n', ' ', text) # Remove carriage returns and line breaks
text = re.sub(r'\s+', ' ', text) # Remove extra spaces
text = re.sub(r'<.*?>', '', text) # Remove any XML tags
text = text.strip().lower() # Strip and convert to lower case
return text
def chatbot(query):
query = clean_text(query)
input_ids = tokenizer(query,return_tensors="pt",max_length=256,truncation=True)
inputs = {key: value.to(device) for key, value in input_ids.items()}
outputs = model.generate(
input_ids["input_ids"],
max_length=1024,
num_beams=5,
early_stopping=True
)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
chatbot,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
],
)
if __name__ == "__main__":
demo.launch()
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