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from turtle import title
import requests
from io import BytesIO
import gradio as gr
from transformers import pipeline
import numpy as np
from PIL import Image
import spaces
pipe = pipeline("zero-shot-image-classification", model="patrickjohncyh/fashion-clip")
images="dog.jpg"
@spaces.GPU
def shot(input, labels_text):
# Check if the input is a URL or an uploaded image
if isinstance(input, str) and (input.startswith("http://") or input.startswith("https://")):
# Input is a URL
response = requests.get(input)
PIL_image = Image.open(BytesIO(response.content)).convert('RGB')
else:
# Input is an uploaded image
PIL_image = Image.fromarray(np.uint8(input)).convert('RGB')
# Split labels into a list
labels = labels_text.split(",")
# Perform the zero-shot image classification
res = pipe(images=PIL_image,
candidate_labels=labels,
hypothesis_template="This is a photo of a {}")
# Return the classification results as a dictionary
return {dic["label"]: dic["score"] for dic in res}
# Define the Gradio interface
iface = gr.Interface(
fn=shot,
inputs=[
gr.inputs.Textbox(label="Image URL (starting with http/https) or Upload Image"),
"text"
],
outputs="label",
examples=[
["https://example.com/dog.jpg", "dog,cat,bird"],
["https://example.com/germany.jpg", "germany,belgium,colombia"],
["https://example.com/colombia.jpg", "germany,belgium,colombia"]
],
description="Add an image URL (starting with http/https) or upload a picture, and provide a list of labels separated by commas.",
title="Zero-shot Image Classification"
)
# Launch the interface
iface.launch() |