# 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()