Create app.py

app.py

@@ -0,0 +1,104 @@
+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()