Screen Recorder Gradio Custom Component
Project description
tags: [custom-component-track, gradio-custom-component, screen-recorder, PIP, picture-in-picture] title: gradio_screenrecorder short_description: Screen Recorder with Picture in Picture Gradio Custom Component colorFrom: blue colorTo: yellow sdk: gradio pinned: false app_file: space.py
gradio_screenrecorder
Screen Recorder Gradio Custom Component
Installation
pip install gradio_screenrecorder
Usage
import gradio as gr
from gradio_screenrecorder import ScreenRecorder
def handle_recording(recording_data):
"""Handle recorded video data"""
print(f'Received recording data: {recording_data}')
if not recording_data or not recording_data.get('video'):
return None
try:
video_info = recording_data['video']
# Return the video path that can be used by the Video component
return video_info.get('path')
except Exception as e:
print(f'Error processing recording: {e}')
return None
css = """
.screen-recorder-demo {
max-width: 800px;
margin: 0 auto;
}
"""
with gr.Blocks(css=css, title="Screen Recorder Demo") as demo:
gr.HTML("""
<h1 style='text-align: center'>
Gradio Screen Recorder Component Demo
</h1>
""")
with gr.Row():
with gr.Column():
recorder = ScreenRecorder(
audio_enabled=True,
webcam_overlay=True, # Disabled for now
webcam_position="top-left",
recording_format="webm",
max_duration=60,
label="Screen Recorder"
)
with gr.Column():
output_video = gr.Video(label="Recorded Video")
# Event handler
recorder.change(
fn=handle_recording,
inputs=recorder,
outputs=output_video
)
if __name__ == "__main__":
demo.launch()
ScreenRecorder
Initialization
| name | type | default | description |
|---|---|---|---|
audio_enabled |
bool
|
True |
None |
webcam_overlay |
bool
|
False |
None |
webcam_position |
"top-left" | "top-right" | "bottom-left" | "bottom-right"
|
"bottom-right" |
None |
recording_format |
str
|
"webm" |
None |
max_duration |
typing.Optional[int][int, None]
|
None |
None |
interactive |
bool
|
True |
None |
Events
| name | description |
|---|---|
record_start |
|
record_stop |
|
stream_update |
|
change |
User function
The impact on the users predict function varies depending on whether the component is used as an input or output for an event (or both).
- When used as an Input, the component only impacts the input signature of the user function.
- When used as an output, the component only impacts the return signature of the user function.
The code snippet below is accurate in cases where the component is used as both an input and an output.
def predict(
value: typing.Optional[
gradio_screenrecorder.screenrecorder.ScreenRecorderData
][ScreenRecorderData, None]
) -> Unknown:
return value
ScreenRecorderData
class ScreenRecorderData(GradioModel):
video: Optional[FileData] = None
duration: Optional[float] = None
audio_enabled: bool = True
status: Literal["recording", "stopped", "error"] = (
"stopped"
)
class Config:
json_encoders = {
FileData: lambda v: v.model_dump()
if v
else None
}
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