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


pipes = {
    "ViT/B-16": pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch16"),
    "ViT/L-14": pipeline("zero-shot-image-classification", model="openai/clip-vit-base-patch16"),
}
inputs = [
    gr.Image(type='pil', 
                    label="Image"),
    gr.Textbox(lines=1, 
                      label="Candidate Labels"),
    gr.Radio(choices=[
                                "ViT/B-16",
                                "ViT/L-14", 
                            ], type="value", label="Model"), 
    gr.Textbox(lines=1, 
                      label="Prompt Template Prompt", 
                      share=False,
                      value="a photo of a {}"),
]
images="festival.jpg"

def shot(image, labels_text, model_name, hypothesis_template):
    labels = [label.strip(" ") for label in labels_text.strip(" ").split(",")]
    res = pipes[model_name](images=image, 
           candidate_labels=labels,
           hypothesis_template=hypothesis_template)
    return {dic["label"]: dic["score"] for dic in res}

iface = gr.Interface(shot, 
            inputs, 
            "label", 
            examples=[["festival.jpg", "lantern, firecracker, couplet", "ViT/B-16", "a photo of a {}"]], 
            description="""<p>Chinese CLIP is a contrastive-learning-based vision-language foundation model pretrained on large-scale Chinese data. For more information, please refer to the paper and official github. Also, Chinese CLIP has already been merged into Huggingface Transformers! <br><br>
            Paper: <a href='https://arxiv.org/pdf/2403.02714'>https://arxiv.org/pdf/2403.02714</a> <br>
            To begin with the demo, provide a picture (either upload manually, or select from the given examples) and add class labels one by one. Optionally, you can also add template as a prefix to the class labels. <br>""",
            title="Cross-Domain Recognition")

iface.launch()