added app.py and dockerfile

Dockerfile

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+FROM python:3.10-slim-bullseye
+RUN apt-get update && apt-get install ffmpeg libsm6 libxext6  -y
+COPY requirements.txt .
+RUN pip install -r requirements.txt
+COPY app.py .
+COPY model.onnx .
+CMD python app.py

app.py

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+import onnxruntime
+import cv2
+import numpy as np
+import oloren as olo
+
+
+class ONNX_Detectron:
+    REQUIRED_WIDTH = 800
+    REQUIRED_HEIGHT = 1043
+
+    def __init__(self, onnx_path):
+        self.onnx_path = onnx_path
+        self.model = onnxruntime.InferenceSession(self.onnx_path)
+
+    def preprocess(self, path):
+        img = np.array(cv2.imread(path))
+        initial_h, initial_w, _ = img.shape
+        img = cv2.resize(img, (self.REQUIRED_WIDTH, self.REQUIRED_HEIGHT), interpolation=cv2.INTER_LINEAR)
+        img = img.transpose((2, 0, 1)).astype(np.float32)
+        return {self.model.get_inputs()[0].name: img}, initial_w, initial_h
+
+    def predict(self, image):
+        prepared_input, input_w, input_h = self.preprocess(image)
+        bboxes, labels, confidence_scores, _ = self.model.run(None, prepared_input)
+        regions = self.postprocess(bboxes, labels, confidence_scores, input_w, input_h)
+        return regions
+
+    def postprocess(
+        self,
+        bboxes: np.ndarray,
+        labels: np.ndarray,
+        confidence_scores: np.ndarray,
+        input_w: float,
+        input_h: float,
+    ):
+        """Process output into Unstructured class. Bounding box coordinates are converted to
+        original image resolution."""
+        regions = []
+        width_conversion = input_w / self.REQUIRED_WIDTH
+        height_conversion = input_h / self.REQUIRED_HEIGHT
+        for (x1, y1, x2, y2), label, conf in zip(bboxes, labels, confidence_scores):
+            if conf < 0.8:
+                continue
+            region = {
+                "x1": x1 * width_conversion,
+                "y1": y1 * height_conversion,
+                "x2": x2 * width_conversion,
+                "y2": y2 * height_conversion,
+                "confidence": conf,
+                "label": label,
+            }
+
+            regions.append(region)
+
+        return regions
+
+
+@olo.register()
+def predict(image=olo.File()):
+    model = ONNX_Detectron("./model.onnx")
+    return model.predict(image)
+
+
+if __name__ == "__main__":
+    olo.run("detectron")