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app.py

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+import streamlit as st
+from transformers import pipeline
+from PIL import Image
+
+# set page setting
+st.set_page_config(page_title='Smoke & Fire Detection')
+
+# set history var
+if 'history' not in st.session_state:
+    st.session_state.history = []
+
+@st.cache(persist=True)
+def loadModel():
+    pipeline = pipeline(task="image-classification", model="EdBianchi/vit-fire-detection")
+    return pipeline
+
+# PROCESSING
+def compute(image):
+    predictions = pipeline(image)
+
+    with st.container():
+        st.image(image, use_column_width=True)
+
+    with st.container():
+        st.write("#### Different classification outputs at different threshold values:")
+        col1, col2, col6 = st.columns(3)
+        col1.metric(predictions[0]['label'], round(predictions[0]['score']+100, 1)+"%")
+        col2.metric(predictions[1]['label'], round(predictions[1]['score']+100, 1)+"%")
+        col6.metric(predictions[2]['label'], round(predictions[2]['score']+100, 1)+"%")
+    return None
+
+# INIT
+with st.spinner('Loading the model, this could take some time...'):
+    pipeline = loadModel()
+
+# TITLE
+st.write("# Smoke & Fire Detection in Forest Environments")
+st.write("#### Upload an Image to see the classifier in action")
+
+# INPUT IMAGE
+file_name = st.file_uploader("Upload an image")
+if file_name is not None:
+    image = Image.open(file_name)
+    compute(image)
+
+# SIDEBAR
+#st.sidebar.write("""""")

requirements.txt

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+transformers
+torch