| # import tempfile | |
| # import streamlit as st | |
| import gradio as gr | |
| from prediction import smartcities | |
| # # Streamlit Interface | |
| # st.header("Smart City Cars and Bikes detection") | |
| # st.markdown("Upload a video or select the example") | |
| # ## Select video to inference | |
| # file_video = str | |
| # f = st.file_uploader(" Upload a video ", type=["mp4"]) | |
| # if f is not None: | |
| # tfile = tempfile.NamedTemporaryFile(delete=False) | |
| # tfile.write(f.read()) | |
| # file_video = tfile.name | |
| # if st.button("example"): | |
| # file_video = "test_video.mp4" | |
| # ## Process video | |
| # if file_video is not None: | |
| # sc = smartcities() | |
| # output = sc.predict(file_video) | |
| # col1, col2 = st.columns(2) | |
| # if output is not None: | |
| # with col1: | |
| # st.subheader("Input: ") | |
| # # video = open(file_video, "wb") | |
| # # video_bytes = video.read() | |
| # # st.video(video, format="video/mp4") | |
| # # st.video(video_bytes) | |
| # with col2: | |
| # st.subheader("Output: ") | |
| # output_video = open(output, "rb") | |
| # output_bytes = output_video.read() | |
| # st.video(output_bytes, format="video/mp4") | |
| # st.download_button("Download", output_bytes, file_name="output_video.mp4", mime="video/mp4") | |
| def object_detection(input): | |
| sc = smartcities() | |
| output = sc.predict(input) | |
| demo = gr.Interface.load(object_detection, input="video", output="video") | |
| demo.launch() | |