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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() |