| 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']) | |
| 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() |