File size: 1,234 Bytes
1b94d1b ca96fd3 1b94d1b f893273 1b94d1b b0ac41a 1b94d1b |
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 |
import gradio as gr
import numpy as np
from PIL import Image
from transformers import pipeline
# Create the pipeline object
pipe = pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch32")
# Define the function that will be used by the interface
def zero_shot_classification(image, labels_text):
# Convert image to a PIL image object
pil_image = Image.fromarray(np.uint8(image)).convert("RGB")
# Split the labels text into a list of labels
labels = labels_text.split(",")
# Use the pipeline to classify the image with the given labels
res = pipe(
images=pil_image,
candidate_labels=labels,
hypothesis_template= "This is a photo of a {}"
)
# Return a dictionary mapping labels to scores
return {dic["label"]: dic["score"] for dic in res}
# Create the interface
iface = gr.Interface(
zero_shot_classification,
["image", "text"],
"label",
examples=[
["corn.jpg", "corn,wheat,rice"],
],
description="Please add a picture and a list of labels separated by commas to see the zero-shot classification capabilities",
title="Zero-shot Image Classification"
)
# Launch the interface
iface.launch()
|