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from transformers import pipeline
import gradio as gr
clip_models = [
"zer0int/CLIP-GmP-ViT-L-14",
"openai/clip-vit-large-patch14",
"laion/CLIP-ViT-bigG-14-laion2B-39B-b160k",
]
clip_checkpoint = clip_models[0]
clip_detector = pipeline(model=clip_checkpoint, task="zero-shot-image-classification")
def postprocess(output):
return {out["label"]: float(out["score"]) for out in output}
def infer(image, candidate_labels):
candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",")]
clip_out = clip_detector(image, candidate_labels=candidate_labels)
return postprocess(clip_out)
def load_clip_model(modelname):
global clip_detector
try:
clip_detector = pipeline(model=modelname, task="zero-shot-image-classification")
except Exception as e:
raise gr.Error(f"Model load error: {modelname} {e}")
return modelname
with gr.Blocks() as demo:
gr.Markdown("# Test CLIP")
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil")
text_input = gr.Textbox(label="Input a list of labels")
model_input = gr.Dropdown(label="CLIP model", choices=clip_models, value=clip_models[0], allow_custom_value=True, interactive=True)
run_button = gr.Button("Run", visible=True)
with gr.Column():
clip_output = gr.Label(label = "CLIP Output", num_top_classes=3)
examples = [["./baklava.jpg", "baklava, souffle, tiramisu"], ["./cheetah.jpg", "cat, dog"], ["./cat.png", "cat, dog"]]
gr.Examples(
examples = examples,
inputs=[image_input, text_input],
outputs=[clip_output],
fn=infer,
cache_examples=True
)
run_button.click(fn=infer,
inputs=[image_input, text_input],
outputs=[clip_output])
model_input.change(load_clip_model, [model_input], [model_input])
demo.launch() |