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import streamlit as st
from transformers import pipeline
from PIL import Image
import numpy as np
from transformers import AutoFeatureExtractor, AutoModelForImageClassification
classifier = pipeline("text-classification", model="distilbert-base-uncased-finetuned-sst-2-english")
def main():
st.title("text-classification")
with st.form("text_field"):
uploaded_file = st.file_uploader("Upload Files",type=['png','jpeg','jpg'])
clicked = st.form_submit_button("Submit")
if clicked:
uploaded_file = st.file_uploader("Upload Files",type=['png','jpeg','jpg'])
if uploaded_file!=None:
img=Image.open(uploaded_file)
extractor = AutoFeatureExtractor.from_pretrained("yangy50/garbage-classification")
model = AutoModelForImageClassification.from_pretrained("yangy50/garbage-classification")
inputs = extractor(img,return_tensors="pt")
outputs = model(**inputs)
label_num=outputs.logits.softmax(1).argmax(1)
label_num=label_num.item()
st.write("The prediction class is:")
if label_num==0:
st.write("cardboard")
elif label_num==1:
st.write("glass")
elif label_num==2:
st.write("metal")
elif label_num==3:
st.write("paper")
elif label_num==4:
st.write("plastic")
else:
st.write("trash")
st.image(img)
if __name__ == "__main__":
main() |