import gradio as gr
import spaces
import torch
from transformers import AutoProcessor, AutoModelForZeroShotImageClassification
from datasets import load_dataset

dataset = load_dataset("not-lain/embedded-pokemon", split="train")
dataset = dataset.add_faiss_index("embeddings")

device = "cuda" if torch.cuda.is_available() else "cpu"

processor = AutoProcessor.from_pretrained("openai/clip-vit-large-patch14")
model = AutoModelForZeroShotImageClassification.from_pretrained(
    "openai/clip-vit-large-patch14", device_map=device
)


@spaces.GPU
def search(query: str, k: int = 4):
    """a function that embeds a new image and returns the most probable results"""

    pixel_values = processor(images=query, return_tensors="pt")[
        "pixel_values"
    ]  # embed new image
    pixel_values = pixel_values.to(device)
    img_emb = model.get_image_features(pixel_values)[0]  # because 1 element
    img_emb = img_emb.cpu().detach().numpy()  # because datasets only works with numpy

    scores, retrieved_examples = dataset.get_nearest_examples(  # retrieve results
        "embeddings",
        img_emb,  # compare our new embedded query with the dataset embeddings
        k=k,  # get only top k results
    )
    images = retrieved_examples["image"]
    labels = {}
    for i in range(k):
        labels[retrieved_examples["text"][i]] = scores[i]

    return images, labels


demo = gr.Interface(search, inputs="image", outputs=["gallery", "label"])

demo.launch(debug=True)