app.py

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

# Load the model and tokenizer from your Hugging Face Hub repository
model_name = "abdulllah01/outputs"  # Replace with your Hugging Face repo name

# Load the model configuration first and modify it if necessary
config = AutoConfig.from_pretrained(model_name)
if hasattr(config, 'quantization_config'):
    config.quantization_config = None  # Disable any quantization settings

# Load the model and tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, config=config)

# Streamlit interface
st.title("Tech Support Chatbot")
st.write("Ask your technical support questions below:")

# Text input for the question
user_input = st.text_input("Your question:", "")

if user_input:
    # Generate a response using the model
    inputs = tokenizer.encode(user_input, return_tensors="pt")
    response = model.generate(inputs, max_length=100)
    answer = tokenizer.decode(response[0], skip_special_tokens=True)
    st.write("**Answer:**", answer)