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| import gradio as gr | |
| import tensorflow as tf | |
| from transformers import pipeline | |
| inception_net = tf.keras.applications.MobileNetV2() | |
| def classify_image(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) | |
| confidences = {f'{pred_scores[0][i][1]}': float(pred_scores[0][i][2]) for i in range(100)} | |
| return confidences | |
| trans = pipeline("automatic-speech-recognition", model="facebook/wav2vec2-large-xlsr-53-spanish") | |
| def audio_to_text(audio): | |
| text = trans(audio)["text"] | |
| return text | |
| classify = pipeline("text-classification", model="pysentimiento/robertuito-sentiment-analysis") | |
| def text_to_sentiment(text): | |
| return classify(text)[0]["label"] | |
| demo = gr.Blocks() | |
| with demo: | |
| gr.Markdown("Second Demo with Blocks") | |
| with gr.Tabs(): | |
| with gr.TabItem("Transcript audio in spanish"): | |
| with gr.Row(): | |
| audio = gr.Audio(source="microphone", type="filepath") | |
| transcription = gr.Textbox() | |
| button1 = gr.Button("Please transcript") | |
| with gr.TabItem("Sentiment analisys"): | |
| with gr.Row(): | |
| text = gr.Textbox() | |
| label = gr.Label() | |
| button2 = gr.Button("Please sentiment") | |
| button1.click(audio_to_text, inputs=audio, outputs=transcription) | |
| button2.click(text_to_sentiment, inputs=text, outputs=label) | |
| with gr.TabItem("Image classify"): | |
| with gr.Row(): | |
| image = gr.Image(shape=(224,224)) | |
| labelImage = gr.Label(num_top_classes=3) | |
| button3 = gr.Button("Please classify Image") | |
| button3.click(classify_image, inputs=image, outputs=labelImage) | |
| demo.launch() |