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import gradio as gr |
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from PIL import Image |
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import os |
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os.system("pip install openai") |
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import openai |
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whisper = gr.Interface.load(name="spaces/sanchit-gandhi/whisper-large-v2") |
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from share_btn import community_icon_html, loading_icon_html, share_js |
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token = os.environ.get('HF_TOKEN') |
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tts = gr.Interface.load(name="spaces/Flux9665/IMS-Toucan") |
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talking_face = gr.Blocks.load(name="spaces/fffiloni/one-shot-talking-face", api_key=token) |
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def infer(audio, openai_api_key): |
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whisper_result = whisper(audio, None, "translate", fn_index=0) |
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gpt_response = try_api(whisper_result, openai_api_key) |
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audio_response = tts(gpt_response[0], "English Text", "English Accent", "English Speaker's Voice", fn_index=0) |
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portrait_link = talking_face("wise_woman_portrait.png", audio_response, fn_index=0) |
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return gr.Textbox.update(value=whisper_result, visible=True), portrait_link, gr.Textbox.update(value=gpt_response[1], visible=True), gr.update(visible=True) |
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def try_api(message, openai_api_key): |
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try: |
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response = call_api(message, openai_api_key) |
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return response, Fore.GREEN + "no error" |
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except openai.error.Timeout as e: |
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print(f"OpenAI API request timed out: {e}") |
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return "oups", f"OpenAI API request timed out: {e}" |
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except openai.error.APIError as e: |
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print(f"OpenAI API returned an API Error: {e}") |
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return "oups", f"OpenAI API returned an API Error: {e}" |
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except openai.error.APIConnectionError as e: |
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print(f"OpenAI API request failed to connect: {e}") |
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return "oups", f"OpenAI API request failed to connect: {e}" |
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except openai.error.InvalidRequestError as e: |
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print(f"OpenAI API request was invalid: {e}") |
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return "oups", f"OpenAI API request was invalid: {e}" |
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except openai.error.AuthenticationError as e: |
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print(f"OpenAI API request was not authorized: {e}") |
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return "oups", f"OpenAI API request was not authorized: {e}" |
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except openai.error.PermissionError as e: |
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print(f"OpenAI API request was not permitted: {e}") |
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return "oups", f"OpenAI API request was not permitted: {e}" |
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except openai.error.RateLimitError as e: |
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print(f"OpenAI API request exceeded rate limit: {e}") |
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return "oups", f"OpenAI API request exceeded rate limit: {e}" |
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def call_api(message, openai_api_key): |
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print("starting open ai") |
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openai.api_key = openai_api_key |
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response = openai.Completion.create( |
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model="text-davinci-003", |
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prompt=message, |
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temperature=0.5, |
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max_tokens=2048, |
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top_p=1, |
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frequency_penalty=0, |
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presence_penalty=0.6 |
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) |
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return str(response.choices[0].text).split("\n",2)[2] |
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title = """ |
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<div style="text-align: center; max-width: 500px; margin: 0 auto;"> |
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<div |
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style=" |
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display: inline-flex; |
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align-items: center; |
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gap: 0.8rem; |
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font-size: 1.75rem; |
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margin-bottom: 10px; |
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" |
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> |
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<h1 style="font-weight: 600; margin-bottom: 7px;"> |
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GPT Talking Portrait |
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</h1> |
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</div> |
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<p style="margin-bottom: 10px;font-size: 94%;font-weight: 100;line-height: 1.5em;"> |
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Use Whisper to ask, alive portrait responds ! |
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</p> |
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</div> |
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""" |
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with gr.Blocks(css="style.css") as demo: |
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with gr.Column(elem_id="col-container"): |
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gr.HTML(title) |
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gpt_response = gr.Video(label="Talking Portrait response", elem_id="video_out") |
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with gr.Group(elem_id="share-btn-container", visible=False) as share_group: |
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community_icon = gr.HTML(community_icon_html) |
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loading_icon = gr.HTML(loading_icon_html) |
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share_button = gr.Button("Share to community", elem_id="share-btn") |
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error_handler = gr.Textbox(visible=False, show_label=False) |
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with gr.Column(elem_id="col-container-2"): |
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with gr.Row(): |
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record_input = gr.Audio(source="microphone",type="filepath", label="Audio input", show_label=True, elem_id="record_btn") |
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openai_api_key = gr.Textbox(max_lines=1, type="password", label="Your OpenAI API Key", placeholder="sk-123abc...") |
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whisper_tr = gr.Textbox(label="whisper english translation", elem_id="text_inp", visible=False) |
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send_btn = gr.Button("Send my request !") |
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send_btn.click(infer, inputs=[record_input, openai_api_key], outputs=[whisper_tr, gpt_response, error_handler, share_group]) |
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share_button.click(None, [], [], _js=share_js) |
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demo.queue(max_size=32, concurrency_count=20).launch(debug=True) |
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