Update app.py

app.py

@@ -1,21 +1,13 @@
 import streamlit as st
 from transformers import pipeline
 import nltk
-from nltk.corpus import stopwords
-from nltk.tokenize import word_tokenize
 
-# Download necessary NLTK data
 nltk.download('punkt')
 nltk.download('stopwords')
 
-
-# Load a pre-trained Hugging Face model
 chatbot = pipeline("text-generation", model="distilgpt2")
 
-
-# Define healthcare-specific response logic (or use a model to generate responses)
 def healthcare_chatbot(user_input):
-    # Simple rule-based keywords to respond
     if "symptom" in user_input:
         return "It seems like you're experiencing symptoms. Please consult a doctor for accurate advice."
     elif "appointment" in user_input:
@@ -23,22 +15,12 @@ def healthcare_chatbot(user_input):
     elif "medication" in user_input:
         return "It's important to take your prescribed medications regularly. If you have concerns, consult your doctor."
     else:
-        # For other inputs, use the Hugging Face model to generate a response
         response = chatbot(user_input, max_length=300, num_return_sequences=1)
-        # Specifies the maximum length of the generated text response, including the input and the generated tokens.
-        # If set to 3, the model generates three different possible responses based on the input.
         return response[0]['generated_text']
 
-
-# Streamlit web app interface
 def main():
-    # Set up the web app title and input area
     st.title("Healthcare Assistant Chatbot")
-    
-    # Display a simple text input for user queries
     user_input = st.text_input("How can I assist you today?", "")
-    
-    # Display chatbot response
     if st.button("Submit"):
         if user_input:
             st.write("User: ", user_input)