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import gradio as gr
import tensorflow as tf
from transformers import pipeline
inception_net = tf.keras.applications.MobileNetV2()
def classify_imagen(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
trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish")
def audio2text(audio):
text = trans(audio)["text"]
return text
classificator = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis")
def text2sentiment(text):
return classificator(text)[0]['label']
demo = gr.Blocks()
with demo:
gr.Markdown("Este es un 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 sentimientos"):
with gr.Row():
texto = gr.Textbox()
label = gr.Label()
b2 = gr.Button("Sentimientos")
b1.click(audio2text, inputs=audio, outputs=transcript)
b2.click(text2sentiment, 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("Clasificar")
bimage.click(classify_imagen, inputs=image, outputs=label)
demo.launch()