Spaces:
Sleeping
Sleeping
File size: 1,726 Bytes
e06fb68 9338a37 1b1c1e7 c49fc60 9338a37 1b1c1e7 9338a37 1b1c1e7 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 |
import gradio as gr
from PIL import Image
from transformers import pipeline
transcribe = pipeline("automatic-speech-recognition", model = "facebook/wav2vec2-large-xlsr-53-spanish")
classifier = pipeline("text-classification", model = "pysentimiento/robertuito-sentiment-analysis")
image_classifier = pipeline("image-classification", model="microsoft/swin-tiny-patch4-window7-224")
def audio_to_text(audio):
text = transcribe(audio)["text"]
return text
def text_to_sentiment(text):
return classifier(text)[0]["label"]
def classify_image(image):
image = Image.fromarray(image.astype('uint8'), 'RGB')
answers = image_classifier(image)
labels = {answer["label"]: answer["score"] for answer in answers}
return labels
demo = gr.Blocks()
with demo:
gr.Markdown("Example with Gradio Blocks")
with gr.Tabs():
with gr.TabItem("Transcribe audio in Spanish"):
with gr.Row():
audio = gr.Audio(sources="microphone", type="filepath")
transcription = gr.Textbox()
transcribeButton = gr.Button("Transcribe")
with gr.TabItem("Sentiment analysis in English and Spanish"):
with gr.Row():
text = gr.Textbox()
label = gr.Label()
sentimentButton = gr.Button("Calculate sentiment")
with gr.TabItem("Image Classification"):
with gr.Row():
image = gr.Image(label="Upload an image here")
label_image = gr.Label(num_top_classes=3)
classifyButton = gr.Button("Classify image")
transcribeButton.click(audio_to_text, inputs = audio, outputs=transcription)
sentimentButton.click(text_to_sentiment, inputs=text, outputs=label)
classifyButton. click(classify_image, inputs=image, outputs=label_image)
demo.launch()
|