app.py

import streamlit as st
import torch
from transformers import DistilBertTokenizer, DistilBertForSequenceClassification

# Load the model and tokenizer
@st.cache_resource  # Cache model for efficiency
def load_model():
    tokenizer = DistilBertTokenizer.from_pretrained('distilbert-base-uncased')
    model = DistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
    return tokenizer, model

tokenizer, model = load_model()

# Input/Output areas
st.title("Simple  Chatbot")
user_input = st.text_input("Enter your message:")

# Preprocess and generate response when the user hits Enter
if user_input:
    if user_input.lower() == "quit":
        st.stop()

    # Encode the user input 
    input_ids = tokenizer.encode(user_input, return_tensors='pt') 

    # Generate a response (adjust parameters for control)
    output_sequences = model.generate(
        input_ids=input_ids,
        max_length=50,  # Example max response length
        temperature=0.8,  # Controls creativity
        # ... other generation parameters ...
    )

    # Decode the generated text and display
    generated_text = tokenizer.decode(output_sequences[0], skip_special_tokens=True)  
    st.write(generated_text)