SadTalker / test_api.py
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import gradio as gr
def call_sadtalker_api(source_image, driven_audio, preprocess_type, is_still_mode, enhancer, batch_size, size_of_image, pose_style, facerender, exp_weight, use_ref_video, ref_video, ref_info, use_idle_mode, length_of_audio, blink_every):
# Define the API endpoint
api_url = "http://localhost:7860/api/predict"
# Prepare the payload
payload = {
"source_image": source_image,
"driven_audio": driven_audio,
"preprocess_type": preprocess_type,
"is_still_mode": is_still_mode,
"enhancer": enhancer,
"batch_size": batch_size,
"size_of_image": size_of_image,
"pose_style": pose_style,
"facerender": facerender,
"exp_weight": exp_weight,
"use_ref_video": use_ref_video,
"ref_video": ref_video,
"ref_info": ref_info,
"use_idle_mode": use_idle_mode,
"length_of_audio": length_of_audio,
"blink_every": blink_every
}
# Make the API request
response = requests.post(api_url, json=payload)
result = response.json()
# Return the generated video URL
return result["data"]
# Create the Gradio interface
iface = gr.Interface(
fn=call_sadtalker_api,
inputs=[
gr.Image(type="filepath", label="Source Image"),
gr.Audio(type="filepath", label="Driven Audio"),
gr.Radio(["crop", "resize", "full", "extcrop", "extfull"], label="Preprocess Type"),
gr.Checkbox(label="Still Mode"),
gr.Checkbox(label="Enhancer"),
gr.Slider(minimum=1, maximum=10, step=1, label="Batch Size"),
gr.Radio([256, 512], label="Size of Image"),
gr.Slider(minimum=0, maximum=45, step=1, label="Pose Style"),
gr.Radio(["facevid2vid", "pirender"], label="Face Render"),
gr.Slider(minimum=0, maximum=3, step=0.1, label="Expression Weight"),
gr.Checkbox(label="Use Reference Video"),
gr.Video(label="Reference Video"),
gr.Radio(["pose", "blink", "pose+blink", "all"], label="Reference Info"),
gr.Checkbox(label="Use Idle Mode"),
gr.Number(label="Length of Audio"),
gr.Checkbox(label="Blink Every")
],
outputs=gr.Video(label="Generated Video"),
title="SadTalker API Client"
)
# Launch the interface
iface.launch()