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import gradio as gr
from gradio_client import Client, handle_file
from huggingface_hub import HfApi
import time
api = HfApi()
repo_ids = ["HuggingFaceH4/idefics2-8b-playground", "HuggingFaceH4/idefics2-8b-vdpoed-playground"]
for repo_id in repo_ids:
if api.space_info(repo_id).runtime.stage != "RUNNING":
api.restart_space(repo_id="HuggingFaceH4/idefics2-8b-playground")
for repo_id in repo_ids:
if api.space_info(repo_id).runtime.stage != "RUNNING":
time.sleep(1)
client_idefics2 = Client("HuggingFaceH4/idefics2-8b-playground")
client_idefics2_dpoed = Client("HuggingFaceH4/idefics2-8b-vdpoed-playground")
def respond(multimodal_input):
x = {"text": multimodal_input["text"], "files": [handle_file(file) for file in multimodal_input["files"]]}
text_1 = client_idefics2.predict(x, api_name="/predict")
text_2 = client_idefics2_dpoed.predict(x, api_name="/predict")
return text_1, text_2
gr.Interface(
respond,
inputs=[gr.MultimodalTextbox(file_types=["image"], show_label=False)],
outputs=[gr.Textbox(label="idefics2-8b"), gr.Textbox(label="idefics2-8b-dpoed")],
).launch()
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