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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.")
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