Create functions.py

functions.py

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+import tensorflow as tf
+import requests
+from numpy import asarray
+from transformers import pipeline
+
+inception_net = tf.keras.applications.MobileNetV2()
+
+# Obteniendo las labels de "https://git.io/JJkYN"
+response = requests.get("https://git.io/JJkYN")
+labels = response.text.split("\n")
+
+trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
+clasificador = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
+
+def classify_image(inp):
+  inp = asarray(inp.resize((224, 224)))
+  inp = inp.reshape((-1,) + inp.shape)
+  inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp)
+  prediction = inception_net.predict(inp).flatten()
+  confidences = {labels[k]: float(prediction[k]) for k in range(1000)}
+  return confidences
+
+def audio2text(audio):
+  text = trans(audio)["text"]
+  return text
+
+def text2sentiment(text):
+  return clasificador(text)[0]["label"]