| import streamlit as st | |
| from pipeline import detectPipeline | |
| st.title('Sign Language Letters detection') | |
| st.write('Detects Sign language Alphabets in an image \nPowered by YOLOv8 Nano model') | |
| st.write('') | |
| detect_pipeline = detectPipeline() | |
| st.info('Sign Language Letters detection model loaded successfully!') | |
| uploaded_file = st.file_uploader("Upload an image", type=["jpg", "png", "jpeg"]) | |
| if uploaded_file is not None: | |
| with st.container(): | |
| col1, col2 = st.columns([3, 3]) | |
| col1.header('Input Image') | |
| col1.image(uploaded_file, caption='Uploaded Image', use_column_width=True) | |
| col1.text('') | |
| col1.text('') | |
| if st.button('Detect'): | |
| detections = detect_pipeline.detect_signs(img_path=uploaded_file) | |
| detections_img = detect_pipeline.drawDetections2Image(img_path=uploaded_file, detections=detections) | |
| col2.header('Detections') | |
| col2.image(detections_img, caption='Predictions by model', use_column_width=True) | |