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import gradio as gr
from huggingface_hub import InferenceClient
"""
For more information on `huggingface_hub` Inference API support, please check the docs:
https://huggingface.co./docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("damo-vilab/modelscope-text-to-video-synthesis")
def respond(
message,
history: list[tuple[str, str]],
system_message,
max_tokens,
temperature,
top_p,
):
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
# NOTE: Video models don't usually use "streaming" generation, so we'll just call once
payload = {
"inputs": message,
"parameters": {
"max_new_tokens": max_tokens,
"temperature": temperature,
"top_p": top_p,
}
}
# Post directly to the model
response = client.post(json=payload)
video_url = response.get("video", None)
if video_url:
yield video_url
else:
yield "Failed to generate video."
"""
For information on how to customize the ChatInterface, peruse the gradio docs:
https://www.gradio.app/docs/chatinterface
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are generating a creative video.", label="System message"),
gr.Slider(minimum=1, maximum=1000, value=250, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=2.0, value=1.0, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.9,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch() |