app.py

import streamlit as st
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
model_name = "Tom158/Nutri_Assist"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
tokenizer = AutoTokenizer.from_pretrained(model_name)

# Set pad token if not already set
if model.config.pad_token_id is None:
    model.config.pad_token_id = model.config.eos_token_id

# Streamlit App Interface
st.title("Nutrition Chatbot")
user_input = st.text_input("Ask me about nutrition:")

if user_input:
    # Use encode_plus to get both input_ids and attention_mask
    inputs = tokenizer.encode_plus(user_input, return_tensors="pt", padding=True, truncation=True)
    
    input_ids = inputs['input_ids']
    attention_mask = inputs['attention_mask']
    
    # Generate output with attention mask and pad token ID
    outputs = model.generate(input_ids, attention_mask=attention_mask, max_length=50)
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    # Display answer
    st.write("Answer:", answer)