import gradio as gr from transformers import pipeline trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish") clasificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis") def audio_to_text(audio): text = trans(audio)["text"] return text def text_to_feel(text): return clasificador(text)[0]["label"] demo = gr.Blocks() with demo: gr.Markdown("Demo de blocks") with gr.Tabs(): with gr.TabItem("trans"): with gr.Row(): audio = gr.Audio(source="microphone", type="filepath") transcription = gr.Textbox() b1 = gr.Button("Transcribe pf") with gr.TabItem("sent"): with gr.Row(): texto = gr.Textbox() label = gr.Label() b2 = gr.Button("Sent porfa") b1.click(audio_to_text, inputs=audio, outputs=transcription) b2.click(text_to_feel, inputs=texto, outputs=label) demo.launch()