app.py

from typing import Any
import pytorch_lightning as pl
from torchvision.models import efficientnet_v2_s, EfficientNet_V2_S_Weights
import torch
from torch import nn
from torchvision import transforms
import yaml
from yaml.loader import SafeLoader
import gradio as gr
import os


class WeedModel(pl.LightningModule):
    def __init__(self, params):
        super().__init__()
        self.params = params

        model = self.params["model"]

        if model.lower() == "efficientnet":
            if self.params["pretrained"]:
                self.base_model = efficientnet_v2_s(
                    weights=EfficientNet_V2_S_Weights.IMAGENET1K_V1
                )
            else:
                self.base_model = efficientnet_v2_s(weights=None)
            num_ftrs = self.base_model.classifier[-1].in_features
            self.base_model.classifier[-1] = nn.Linear(num_ftrs, self.params["n_class"])

        else:
            print("not prepared model yet!!")

    def forward(self, x):
        embedding = self.base_model(x)
        return embedding

    def predict_step(
        self, batch: Any, batch_idx: int = 0, dataloader_idx: int = 0
    ) -> Any:
        y_hat = self(batch)
        preds = torch.softmax(y_hat, dim=-1).tolist()

        # preds = torch.argmax(preds, dim=-1)
        return preds


def predict(image):

    tensor_image = transform(image)
    outs = model.predict_step(tensor_image.unsqueeze(0))
    labels = {class_names[k]: float(v) for k, v in enumerate(outs[0][:-1])}

    return labels


title = " AISeed AI Application Demo "
description = "# A Demo of Deep Learning for Weed Classification"
example_list = [["examples/" + example] for example in os.listdir("examples")]

with open("class_names.txt", "r", encoding="utf-8") as f:
    class_names = f.read().splitlines()

with gr.Blocks() as demo:
    demo.title = title
    gr.Markdown(description)
    with gr.Tabs():
        with gr.TabItem("Images"):
            with gr.Row():
                with gr.Column():
                    im = gr.Image(type="pil", label="input image", sources=["upload", "webcam"])
                with gr.Column():
                    label_conv = gr.Label(label="Predictions", num_top_classes=4)
                    btn = gr.Button(value="predict")
            btn.click(predict, inputs=im, outputs=[label_conv])
            gr.Examples(examples=example_list, inputs=[im], outputs=[label_conv])
 

if __name__ == "__main__":
    with open("config.yaml") as f:
        PARAMS = yaml.load(f, Loader=SafeLoader)
        print(PARAMS)
    model = WeedModel.load_from_checkpoint(
        "model/epoch=08.ckpt", params=PARAMS, map_location=torch.device("cpu")
    )
    model.eval()

    transform = transforms.Compose(
        [
            transforms.Resize(256),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            # transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
        ]
    )

    demo.launch()