app.py

import os
import warnings

import gradio as gr
import lightning as L
import numpy as np
from lightning.app.components.serve import ServeGradio
from PIL import Image

from lang_sam import LangSAM
from lang_sam import SAM_MODELS
from lang_sam.utils import draw_image
from lang_sam.utils import load_image

warnings.filterwarnings("ignore")


class LitGradio(ServeGradio):

    inputs = [
        gr.Dropdown(choices=list(SAM_MODELS.keys()), label="SAM model", value="vit_h"),
        gr.Slider(0, 1, value=0.3, label="Box threshold"),
        gr.Slider(0, 1, value=0.25, label="Text threshold"),
        gr.Image(type="filepath", label='Image'),
        #gr.inputs.File(file_count="multiple", label="Image list"),
        gr.Textbox(lines=1, label="Text Prompt"),
    ]
    outputs = [gr.outputs.Image(type="pil", label="Output Image")]

    def __init__(self, sam_type="vit_h"):
        super().__init__()
        self.ready = False
        self.sam_type = sam_type

    def predict(self, sam_type, box_threshold, text_threshold, image_path, text_prompt):
        print("Predicting... ", sam_type, box_threshold, text_threshold, image_path, text_prompt)
        if sam_type != self.model.sam_type:
            self.model.build_sam(sam_type)
        
        image_pil = load_image(image_path)

        #image_pil2 = load_image(image_list[0])
        #print(image_list[0].name)

        masks, boxes, phrases, logits = self.model.predict(image_pil, text_prompt, box_threshold, text_threshold)
        labels = [f"{phrase} {logit:.2f}" for phrase, logit in zip(phrases, logits)]
        image_array = np.asarray(image_pil)
        image = draw_image(image_array, masks, boxes, labels)
        image = Image.fromarray(np.uint8(image)).convert("RGB")
        return image

    def build_model(self, sam_type="vit_h"):
        model = LangSAM(sam_type)
        self.ready = True
        return model


app = L.LightningApp(LitGradio())