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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=[
["dog.jpg", "dog,cat,horse,zebra"],
],
description="Add a picture and a list of labels separated by commas",
title="Zero-shot Image Classification"
)
# Launch the interface
iface.launch()
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