Update app.py

app.py

@@ -4,18 +4,22 @@ import re
 import torch
 from transformers import T5Tokenizer, T5ForConditionalGeneration
 
+# Load pre-trained model and tokenizer
 tokenizer = T5Tokenizer.from_pretrained("ahmed792002/Finetuning_T5_HealthCare_Chatbot")
 model = T5ForConditionalGeneration.from_pretrained("ahmed792002/Finetuning_T5_HealthCare_Chatbot")
 
+# Function to clean input text
 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
+
+# Chatbot function
 def chatbot(query):
     query = clean_text(query)
-    input_ids = tokenizer(query,return_tensors="pt",max_length=256,truncation=True)
+    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"],
@@ -24,7 +28,7 @@ def chatbot(query):
         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
 """
@@ -35,6 +39,5 @@ demo = gr.ChatInterface(
     ],
 )
 
-
 if __name__ == "__main__":
     demo.launch()