Delete app.py

app.py

@@ -1,60 +0,0 @@
-import streamlit as st
-from transformers import pipeline as pip
-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()
-def demo_img():
-    demo_img = Image.open("./demo.jpg")
-    compute(demo_img)
-
-@st.cache(persist=True)
-def loadModel():
-    pipeline = pip(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("### Classification Outputs:")
-        col1, col2, col6 = st.columns(3)
-        col1.metric(predictions[0]['label'], str(round(predictions[0]['score']*100, 1))+"%")
-        col2.metric(predictions[1]['label'], str(round(predictions[1]['score']*100, 1))+"%")
-        col6.metric(predictions[2]['label'], str(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 and Fire in Forests 🌲")
-st.write("""Wildfires or forest fires are **unpredictable catastrophic and destructive** events that affect **rural areas**. 
-The impact of these events affects both **vegetation and wildlife**. 
-
-This application showcases the **vit-fire-detection** model, a version of google **vit-base-patch16-224-in21k** vision transformer fine-tuned for **smoke and fire detection**. In particular, we can imagine a setup in which webcams, drones, or other recording devices **take pictures of a wild environment every t seconds or minutes**. The proposed system is then able to classify the current situation as **normal, smoke, or fire**. 
-""")
-
-st.write("### Upload an image to see the classifier in action")
-# INPUT IMAGE
-file_name = st.file_uploader("")
-if file_name is not None:
-    image = Image.open(file_name)
-    compute(image)
-
-# DEMO IMAGE
-demo_img()
-
-# SIDEBAR
-#st.sidebar.write("""""")