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()  # End the Streamlit app

    encoded_input = tokenizer(user_input, return_tensors='pt')
    outputs = model(**encoded_input)
    logits = outputs.logits
    predicted_class_id = logits.argmax(-1).item()

    # # Example sentiment mapping (you might have your own)
    # sentiment_map = {0: "negative", 1: "neutral", 2: "positive"}
    # sentiment = sentiment_map[predicted_class_id]

    st.write("outputs")