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| import gradio as gr | |
| import tensorflow as tf | |
| from transformers import pipeline | |
| inception_net = tf.keras.applications.MobileNetV2() | |
| def clasificador_imagenes(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 | |
| def audio_a_texto(audio): | |
| text = trans(audio)["text"] | |
| return text | |
| def texto_a_sentimiento(text): | |
| return classificator(text)[0]['label'] | |
| trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish") | |
| classificator = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis") | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("# Demo con Blocks") | |
| with gr.Tabs(): | |
| with gr.TabItem("Transcribe Audio en espa帽ol"): | |
| with gr.Row(): | |
| audio = gr.Audio(source='microphone', type='filepath') | |
| transcript = gr.Textbox() | |
| b1 = gr.Button("Transcribe") | |
| with gr.TabItem("Analisis de sentimiento"): | |
| with gr.Row(): | |
| texto = gr.Textbox() | |
| label = gr.Label() | |
| b2 = gr.Button("Sentimiento") | |
| b1.click(audio_a_texto, inputs=audio, outputs=transcript) | |
| b2.click(texto_a_sentimiento, 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("Clasifica") | |
| bimage.click(clasificador_imagenes, inputs=image, outputs=label) | |
| demo.launch() |