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| import gradio as gr | |
| import requests | |
| from transformers import pipeline | |
| import tensorflow as tf | |
| # Obteniendo las labels de "https://git.io/JJkYN" | |
| respuesta = requests.get("https://raw.githubusercontent.com/gradio-app/mobilenet-example/master/labels.txt") | |
| etiquetas =respuesta.text.split("\n") | |
| # models | |
| trans = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish") | |
| clasificador = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis") | |
| inception_net = tf.keras.applications.MobileNetV2() | |
| def clasifica_imagen(inp): | |
| inp = inp.reshape((-1,224,224,3)) | |
| inp = tf.keras.applications.mobilenet_v2.preprocess_input(inp) | |
| prediction = inception_net.predict(inp).flatten() | |
| confidences ={etiquetas[i]: float(prediction[i]) for i in range(1000)} | |
| return confidences | |
| def audio_a_text(audio): | |
| text = trans(audio)["text"] | |
| return text | |
| def texto_a_sentimiento(text): | |
| return clasificador(text)[0]["label"] | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("Este es el segundo demo con Blocks") | |
| with gr.Tabs(): | |
| with gr.TabItem("Transcribe audio en español"): | |
| with gr.Row(): | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| transcripcion = gr.Textbox() | |
| b1 = gr.Button("Transcribe porfa") | |
| with gr.TabItem("Análisis de sentimiento en español"): | |
| with gr.Row(): | |
| texto = gr.Textbox() | |
| label = gr.Label() | |
| b2 = gr.Button("Sentimiento porfa") | |
| with gr.TabItem("clasificación de imágenes"): | |
| with gr.Row(): | |
| image = gr.Image(shape=(224,224)) | |
| label = gr.Label(num_top_classes=3) | |
| b3 = gr.Button("clasificar") | |
| b1.click(audio_a_text, inputs = audio, outputs=transcripcion) | |
| b2.click(texto_a_sentimiento, inputs=texto, outputs=label) | |
| b3.click(clasifica_imagen, inputs=image, outputs=label) | |
| demo.launch() |