Create app.py

Platzi Project

app.py

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+from transformers import pipeline
+import gradio as gr
+
+trans = pipeline("automatic-speech-recognition", model ="facebook/wav2vec2-large-xlsr-53-spanish")
+traductor = pipeline("translation", model = "Helsinki-NLP/opus-mt-es-en")
+ner = pipeline("ner", model = "d4data/biomedical-ner-all")
+
+def audio2text(audio):
+    text = trans(audio)["text"]
+    return text
+
+def text2eng(text):
+    return traductor(text)[0]["translation_text"]
+
+def eng2ner(text):
+    output = ner(text)
+    return {"text": text, "entities": output}
+
+demo = gr.Blocks()
+
+with demo:
+    gr.Markdown("Demo sobre historia clínica en español a entidades en ingles")
+    audio = gr.Audio(sources="microphone", type="filepath")
+    texto = gr.Textbox()
+    b_text = gr.Button("Transcribir")
+    b_text.click(audio2text, inputs=audio, outputs=texto)
+
+    transcripcion = gr.Textbox()
+    b_trans = gr.Button("Traducir historia")
+    b_trans.click(text2eng, inputs=texto, outputs=transcripcion)
+
+    entidades = gr.HighlightedText()
+    b_ner =gr.Button("Search entities")
+    b_ner.click(eng2ner, inputs=transcripcion, outputs=entidades)
+
+demo.launch()