from turtle import title
import gradio as gr
from transformers import pipeline
import numpy as np
from PIL import Image


pipes = {
    "chinese-clip-vit-base-patch16": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-base-patch16"),
    "chinese-clip-vit-large-patch14": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-large-patch14"),
    "chinese-clip-vit-large-patch14-336px": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-large-patch14-336px"),
    "chinese-clip-vit-huge-patch14": pipeline("zero-shot-image-classification", model="OFA-Sys/chinese-clip-vit-huge-patch14"),
}
images="festival.jpg"

def shot(image, labels_text):
    PIL_image = Image.fromarray(np.uint8(image)).convert('RGB')
    labels = labels_text.split(",")
    res = pipes['chinese-clip-vit-base-patch16'](images=PIL_image, 
           candidate_labels=labels,
           hypothesis_template= "{}")
    return {dic["label"]: dic["score"] for dic in res}
    
iface = gr.Interface(shot, 
                    ["image", "text"], 
                    "label", 
                    examples=[["festival.jpg", "灯笼, 鞭炮, 对联"], 
                              ["cat-dog-music.png", "音乐表演, 体育运动"],
                              ["football-match.jpg", "梅西, C罗, 马奎尔"]],
                    description="Add a picture and a list of labels separated by commas",
                    title="Zero-shot Image Classification")

iface.launch()