app.py

import gradio as gr
from transformers import pipeline, ImageClassificationPipeline

class MultiClassLabel(ImageClassificationPipeline):
    def postprocess(self, model_outputs, top_k=5):
        if top_k > self.model.config.num_labels:
            top_k = self.model.config.num_labels

        if self.framework == "pt":
            probs = model_outputs.logits.sigmoid()[0]
            scores, ids = probs.topk(top_k)
        elif self.framework == "tf":
            probs = stable_softmax(model_outputs.logits, axis=-1)[0]
            topk = tf.math.top_k(probs, k=top_k)
            scores, ids = topk.values.numpy(), topk.indices.numpy()
        else:
            raise ValueError(f"Unsupported framework: {self.framework}")

        scores = scores.tolist()
        ids = ids.tolist()
        return [{"score": score, "label": self.model.config.id2label[_id]} for score, _id in zip(scores, ids)]

pipe_aesthetic = pipeline("image-classification", "./sonic", pipeline_class=MultiClassLabel)

def aesthetic(input_img):
    data = pipe_aesthetic(input_img, top_k=5)
    final = {}
    for d in data:
        final[d["label"]] = d["score"]
    return final
demo_aesthetic = gr.Interface(fn=aesthetic, inputs=gr.Image(type="pil"), outputs=gr.Label(label="characters"))

gr.Parallel(demo_aesthetic).launch()