Create app.py

app.py

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+from transformers import pipeline
+import gradio as gr
+from PIL import Image
+
+
+def classify_img(im):
+    im = Image.fromarray(im.astype('uint8'), 'RGB')
+    ans = image_cla(im)
+    labels = {v["label"]: v["score"] for v in ans}
+    return labels
+
+
+def voice2text(audio):
+    text = voice_cla(audio)["text"]
+    return text
+
+
+def text2sentiment(text):
+    sentiment = text_cla(text)[0]["label"]
+    return sentiment
+
+
+def make_block(dem):
+    with dem:
+        gr.Markdown("""
+
+# Ejemplo de `space` multiclassifier: Curso Platzi""")
+        with gr.Tabs():
+            with gr.TabItem("Transcribe audio en español"):
+                with gr.Row():
+                    audio = gr.Audio(source="microphone", type="filepath")
+                    transcripcion = gr.Textbox()
+                b1 = gr.Button("Voz a Texto")
+
+            with gr.TabItem("Análisis de sentimiento en español"):
+                with gr.Row():
+                    texto = gr.Textbox()
+                    label = gr.Label()
+                b2 = gr.Button("Texto a Sentimiento")
+            with gr.TabItem("Clasificación de Imágenes"):
+                with gr.Row():
+                    image = gr.Image(label="Carga una imagen aquí")
+                    label_image = gr.Label(num_top_classes=5)
+                b3 = gr.Button("Clasifica")
+
+            b1.click(voice2text, inputs=audio, outputs=transcripcion)
+            b2.click(text2sentiment, inputs=texto, outputs=label)
+            b3.click(classify_img, inputs=image, outputs=label_image)
+
+
+if __name__ == '__main__':
+    image_cla = pipeline("image-classification", model="microsoft/swin-tiny-patch4-window7-224")
+    voice_cla = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
+    text_cla = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
+
+    demo = gr.Blocks()
+    make_block(demo)
+    demo.launch()
+