import torch
import gradio as gr
from transformers import AlignProcessor, AlignModel


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

processor = AlignProcessor.from_pretrained("kakaobrain/align-base")
model = AlignModel.from_pretrained("kakaobrain/align-base").to(device)
model.eval()


def predict(image, labels):
    labels = labels.split(', ')
    inputs = processor(images=image, text=labels, return_tensors="pt").to(device)

    with torch.no_grad():
        outputs = model(**inputs)

    logits_per_image = outputs.logits_per_image  
    probs = logits_per_image.softmax(dim=1).cpu().numpy()
    return {k: float(v) for k, v in zip(labels, probs[0])}


description = """
<div class="container" style="display:flex;">
  <div class="image">
    <img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/blog/132_vit_align/align.png" alt="ALIGN performance" />
  </div>
  <div class="text">
  <p>Gradio demo for <a href="https://huggingface.co/docs/transformers/main/en/model_doc/align">ALIGN</a>, 
    as introduced in <a href="https://arxiv.org/abs/2102.05918"></a><i>"Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision"</i>. ALIGN features a dual-encoder architecture with EfficientNet and BERT as its text and vision encoders, and learns to align visual and text representations with contrastive learning. 
    Unlike previous work, ALIGN leverages a massive noisy dataset and shows that the scale of the corpus can be used to achieve SOTA representations with a simple recipe.
    \n\nALIGN is not open-sourced and the `kakaobrain/align-base` model used for this demo is based on the Kakao Brain implementation that follows the original paper. The model is trained on the open source [COYO](https://github.com/kakaobrain/coyo-dataset) dataset by the Kakao Brain team. To perform zero-shot image classification with ALIGN, upload an image and enter your candidate labels as free-form text separated by a comma followed by a space.</p>
  </div>
</div>
"""

gr.Interface(
    fn=predict,
    inputs=[
        gr.inputs.Image(label="Image to classify", type="pil"),
        gr.inputs.Textbox(lines=1, label="Comma separated candidate labels", placeholder="Enter labels separated by ', '",)
    ],
    theme="grass",
    outputs="label",
    examples=[
        ["assets/cartoon.jpeg", "dinosaur, drawing, forest",], 
        ["assets/painting.jpeg", "watercolor painting, oil painting, boats",], 
    ],
    title="Zero-Shot Image Classification with ALIGN",
    description=description
).launch()