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import gradio as gr
#import requests
from PIL import Image
import os
token = os.environ.get('HF_TOKEN')
whisper_to_gpt = gr.Blocks.load(name="spaces/fffiloni/whisper-to-chatGPT")
tts = gr.Interface.load(name="spaces/Flux9665/IMS-Toucan")
talking_face = gr.Blocks.load(name="spaces/fffiloni/one-shot-talking-face", api_key=token)
def infer(audio):
gpt_response = whisper_to_gpt(audio, "translate", fn_index=0)
#print(gpt_response)
audio_response = tts(gpt_response[1], "English Text", "English Accent", "English Speaker's Voice", fn_index=0)
#image = Image.open(r"wise_woman_portrait.png")
portrait_link = talking_face("wise_woman_portrait.png", audio_response, fn_index=0)
#portrait_response = requests.get(portrait_link, headers={'Authorization': 'Bearer ' + token})
#print(portrait_response.text)
return portrait_link
title = """
<div style="text-align: center; max-width: 500px; margin: 0 auto;">
<div
style="
display: inline-flex;
align-items: center;
gap: 0.8rem;
font-size: 1.75rem;
margin-bottom: 10px;
"
>
<h1 style="font-weight: 600; margin-bottom: 7px;">
GPT Talking Portrait
</h1>
</div>
<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;">
Use Whisper to ask, alive portrait responds !
</p>
</div>
"""
css = '''
#col-container, #col-container-2 {max-width: 510px; margin-left: auto; margin-right: auto;}
a {text-decoration-line: underline; font-weight: 600;}
div#record_btn > .mt-6 {
margin-top: 0!important;
}
div#record_btn > .mt-6 button {
width: 100%;
height: 40px;
}
.footer {
margin-bottom: 45px;
margin-top: 10px;
text-align: center;
border-bottom: 1px solid #e5e5e5;
}
.footer>p {
font-size: .8rem;
display: inline-block;
padding: 0 10px;
transform: translateY(10px);
background: white;
}
.dark .footer {
border-color: #303030;
}
.dark .footer>p {
background: #0b0f19;
}
'''
with gr.Blocks(css=css) as demo:
with gr.Column(elem_id="col-container"):
gr.HTML(title)
with gr.Row():
record_input = gr.Audio(source="microphone",type="filepath", show_label=True,elem_id="record_btn")
with gr.Row():
send_btn = gr.Button("Send my request !")
with gr.Column(elem_id="col-container-2"):
gpt_response = gr.Video()
send_btn.click(infer, inputs=[record_input], outputs=[gpt_response])
demo.queue(max_size=32, concurrency_count=20).launch(debug=True)
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