app.py

import gradio as gr
import numpy as np
import json
import os
from PIL import Image
import onnxruntime as rt
class ONNXModel:
    def __init__(self, dir_path) -> None:
        """Method to get name of model file. Assumes model is in the parent directory for script."""
        model_dir = os.path.dirname(dir_path)
        with open(os.path.join(model_dir, "signature.json"), "r") as f:
            self.signature = json.load(f)
        self.model_file = os.path.join(model_dir, self.signature.get("filename"))
        if not os.path.isfile(self.model_file):
            raise FileNotFoundError(f"Model file does not exist")
        # get the signature for model inputs and outputs
        self.signature_inputs = self.signature.get("inputs")
        self.signature_outputs = self.signature.get("outputs")
        self.session = None
        if "Image" not in self.signature_inputs:
            raise ValueError("ONNX model doesn't have 'Image' input! Check signature.json, and please report issue to Lobe.")
        # Look for the version in signature file.
        # If it's not found or the doesn't match expected, print a message
        version = self.signature.get("export_model_version")
        if version is None or version != EXPORT_MODEL_VERSION:
            print(
                f"There has been a change to the model format. Please use a model with a signature 'export_model_version' that matches {EXPORT_MODEL_VERSION}."
            )

    def load(self) -> None:
        """Load the model from path to model file"""
        # Load ONNX model as session.
        self.session = rt.InferenceSession(path_or_bytes=self.model_file)

    def predict(self, image: Image.Image) -> dict:
        """
        Predict with the ONNX session!
        """
        # process image to be compatible with the model
        img = self.process_image(image, self.signature_inputs.get("Image").get("shape"))
        # run the model!
        fetches = [(key, value.get("name")) for key, value in self.signature_outputs.items()]
        # make the image a batch of 1
        feed = {self.signature_inputs.get("Image").get("name"): [img]}
        outputs = self.session.run(output_names=[name for (_, name) in fetches], input_feed=feed)
        return self.process_output(fetches, outputs)

    def process_image(self, image: Image.Image, input_shape: list) -> np.ndarray:
        """
        Given a PIL Image, center square crop and resize to fit the expected model input, and convert from [0,255] to [0,1] values.
        """
        width, height = image.size
        # ensure image type is compatible with model and convert if not
        if image.mode != "RGB":
            image = image.convert("RGB")
        # center crop image (you can substitute any other method to make a square image, such as just resizing or padding edges with 0)
        if width != height:
            square_size = min(width, height)
            left = (width - square_size) / 2
            top = (height - square_size) / 2
            right = (width + square_size) / 2
            bottom = (height + square_size) / 2
            # Crop the center of the image
            image = image.crop((left, top, right, bottom))
        # now the image is square, resize it to be the right shape for the model input
        input_width, input_height = input_shape[1:3]
        if image.width != input_width or image.height != input_height:
            image = image.resize((input_width, input_height))

        # make 0-1 float instead of 0-255 int (that PIL Image loads by default)
        image = np.asarray(image) / 255.0
        # format input as model expects
        return image.astype(np.float32)

    def process_output(self, fetches: dict, outputs: dict) -> dict:
        # un-batch since we ran an image with batch size of 1,
        # convert to normal python types with tolist(), and convert any byte strings to normal strings with .decode()
        out_keys = ["label", "confidence"]
        results = {}
        for i, (key, _) in enumerate(fetches):
            val = outputs[i].tolist()[0]
            if isinstance(val, bytes):
                val = val.decode()
            results[key] = val
        confs = results["Confidences"]
        labels = self.signature.get("classes").get("Label")
        output = [dict(zip(out_keys, group)) for group in zip(labels, confs)]
        sorted_output = {"predictions": sorted(output, key=lambda k: k["confidence"], reverse=True)}
        return sorted_output
EXPORT_MODEL_VERSION=1
model = ONNXModel(dir_path="model.onnx")
model.load()

def predict(image):
    image = Image.fromarray(np.uint8(image), 'RGB')
    prediction = model.predict(image)
    for output in prediction["predictions"]:
        output["confidence"] = round(output["confidence"], 2)
    return prediction

inputs = gr.inputs.Image(type="pil")
outputs = gr.outputs.JSON()

gr.Interface(fn=predict, inputs=inputs, outputs=outputs).launch()