Creación de app

app.py

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+import gradio as gr
+import tensorflow as tf
+from transformers import pipeline
+
+inception_net = tf.keras.applications.MobileNetV2()
+def clasificador_imagenes(inp):
+  inp = inp.reshape((-1, 224, 224, 3))
+  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+  prediction = inception_net.predict(inp).reshape(1,1000)
+  pred_scores = tf.keras.applications.mobilenet_v2.decode_predictions(prediction, top=100)
+  confidence = {f'{pred_scores[0][i][1]}': float(pred_scores[0][i][2]) for i in range(100)}  
+  return confidence
+
+
+def audio_a_texto(audio):
+  text = trans(audio)["text"]
+  return text
+    
+
+def texto_a_sentimiento(text):
+  return classificator(text)[0]['label']
+
+
+trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
+classificator = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
+
+
+demo = gr.Blocks()
+
+with demo:
+  gr.Markdown("# Demo con Blocks")
+  with gr.Tabs():
+
+    with gr.TabItem("Transcribe Audio en español"):
+      with gr.Row():
+        audio = gr.Audio(source='microphone', type='filepath')
+        transcript = gr.Textbox()
+      b1 = gr.Button("Transcribe")
+
+    with gr.TabItem("Analisis de sentimiento"):
+      with gr.Row():
+        texto = gr.Textbox()
+        label = gr.Label()
+      b2 = gr.Button("Sentimiento")
+
+    b1.click(audio_a_texto, inputs=audio, outputs=transcript)
+    b2.click(texto_a_sentimiento, inputs=texto, outputs=label)
+
+    with gr.TabItem("Clasificador de imagenes"):
+      with gr.Row():
+        image = gr.Image(shape=(224, 224))
+        label= gr.Label(num_top_classes=3)
+      bimage= gr.Button("Clasifica")
+
+    bimage.click(clasificador_imagenes, inputs=image, outputs=label)
+
+demo.launch()