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import gradio as gr |
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from transformers import pipeline |
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title_emotion = "Classify text according to emotion" |
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description_emotion = "Emotion text classification by Vishal Tiwari " |
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examples_emotion = [ |
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["Remember before Twitter when you took a photo of food, got the film developed, then drove around showing everyone the pic? No? Me neither."], |
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['''"We are all here because we're committed to the biggest question of all: What's out there?" Take your first steps toward answering that question by watching our Gameplay Reveal from the #XboxBethesda Showcase. '''], |
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["A STUNNER IN KNOXVILLE! 😱 Notre Dame takes down No. 1 Tennessee for its first trip to Omaha in 20 years‼️"], |
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["you and I best moment is yet to come 💜 #BTS9thAnniversary"] |
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] |
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interface_emotion = gr.Interface.load( |
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"huggingface/bhadresh-savani/bert-base-go-emotion", |
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title=title_emotion, |
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description=description_emotion, |
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examples=examples_emotion |
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) |
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title_tts = "Text to Speech Translation" |
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examples_tts = [ |
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"I love learning machine learning", |
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"How do you do?", |
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] |
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interface_tts = gr.Interface.load( |
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"huggingface/facebook/fastspeech2-en-ljspeech", |
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title=title_tts, |
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examples=examples_tts, |
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description="Give me something to say!", |
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) |
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demo = gr.TabbedInterface([interface_emotion, interface_tts], ["Emotion Classification", "Text to Speech"]) |
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if __name__ == "__main__": |
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demo.launch() |
