import streamlit as st
from PIL import Image
import torch
pip install torch torchvision torchaudio

from transformers import AutoFeatureExtractor, AutoModelForImageClassification

# Load model and extractor
model = AutoModelForImageClassification.from_pretrained("best.pt")
extractor = AutoFeatureExtractor.from_pretrained("best.pt")

st.title('Smoke Detection App')

uploaded_image = st.file_uploader("Choose an image...", type="jpg")

if uploaded_image is not None:
    image = Image.open(uploaded_image)
    st.image(image, caption='Uploaded Image.', use_column_width=True)
    st.write("")
    st.write("Classifying...")

    inputs = extractor(images=image, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    predictions = torch.argmax(outputs.logits, dim=1)

    if predictions.item() == 1:
        st.write("Smoke detected!")
    else:
        st.write("No smoke detected.")