First commit for demo

app.py

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+'''Imports'''
+import tensorflow as tf
+import requests
+from transformers import pipeline
+import gradio as gr
+
+'''Config inception'''
+inception_net = tf.keras.applications.MobileNetV2()
+
+'''Making request and set database'''
+response = requests.get("https://git.io/JJkYN")
+tags = response.text.split("\n")
+
+'''Define model and classify pipelines'''
+trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
+classify = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
+
+'''Define functions for demo'''
+def classify_image(inp):
+  inp = inp.reshape((-1,224,224,3))
+  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+  prediction = inception_net.predict(inp).flatten()
+  confidences = {tags[i]: float(prediction[i]) for i in range(1000)}
+  return confidences
+
+def audio_to_text(audio):
+  text = trans(audio)["text"]
+  return text
+
+def text_to_sentiment(text):
+  return classify(text)[0]["label"]
+
+'''Define blocks for demo'''
+demo = gr.Blocks()
+
+'''Making demo'''
+with demo:
+  gr.Markdown("Demo for platzi class")
+  audio = gr.Audio(source="microphone", type="filepath")
+  text = gr.Textbox()
+  button1 = gr.Button("Please transcribe")
+  button1.click(audio_to_text, inputs=audio, outputs=text)
+
+  label = gr.Label()
+  button2 = gr.Button("Please classify the sentiment")
+  button2.click(text_to_sentiment, inputs=text, outputs=label)
+
+demo.launch()