added flask backend app

app.py

@@ -0,0 +1,81 @@
+import os
+import cv2
+import json
+import torch
+import logging
+
+import numpy as np
+from flask import Flask, jsonify, request
+from flask.wrappers import Response
+
+from iam_line_recognition.model_main import CRNN
+from iam_line_recognition.utils import ctc_decode
+from iam_line_recognition.dataset import HWRecogIAMDataset
+
+app = Flask("IAM_Handwriting_Recognition")
+logging.basicConfig(level=logging.INFO)
+
+file_model_local = "artifacts/crnn_H_32_W_768_E_196.pth"
+file_model_cont = "/data/models/crnn_H_32_W_768_E_196.pth"
+device = "cpu"
+num_classes = len(HWRecogIAMDataset.LABEL_2_CHAR) + 1
+image_height = 32
+mean_arr = np.array([[0.485, 0.456, 0.406]])
+std_arr = np.array([[0.229, 0.224, 0.225]])
+hw_recog_model = CRNN(num_classes, image_height)
+
+try:
+    logging.info(f"loading model from {file_model_local}")
+    hw_recog_model.load_state_dict(
+        torch.load(file_model_local, map_location=device)
+    )
+except:
+    logging.info(f"loading model from {file_model_cont}")
+    hw_recog_model.load_state_dict(
+        torch.load(file_model_cont, map_location=device)
+    )
+hw_recog_model.to(device)
+hw_recog_model.eval()
+
+
+def predict_hw(img_test: np.ndarray) -> str:
+    img_test = np.expand_dims(img_test, 0)
+    img_test = img_test.astype(np.float32) / 255.0
+    img_test = (img_test - mean_arr) / std_arr
+    img_test = np.transpose(img_test, axes=[0, 3, 1, 2])
+    img_tensor = torch.tensor(img_test).float()
+    img_tensor = img_tensor.to(device, dtype=torch.float)
+    log_probs = hw_recog_model(img_tensor)
+    pred_labels = ctc_decode(log_probs.detach())
+    str_pred = [HWRecogIAMDataset.LABEL_2_CHAR[i] for i in pred_labels[0]]
+    str_pred = "".join(str_pred)
+    return str_pred
+
+@app.route("/predict", methods=["POST"])
+def predict() -> Response:
+    logging.info("IAM Handwriting recognition app")
+    img_file = request.files["image_file"]
+    try:
+        img_arr = np.fromstring(img_file.read(), np.uint8)
+    except:
+        img_arr = np.fromstring(img_file.getvalue(), np.uint8)
+    img_dec = cv2.imdecode(img_arr, cv2.IMREAD_COLOR)
+    img_dec = cv2.cvtColor(img_dec, cv2.COLOR_BGR2RGB)
+
+    img_dec = cv2.resize(img_dec, (768, 32), interpolation = cv2.INTER_LINEAR)
+
+    str_pred = predict_hw(img_dec)
+
+    dict_pred = {
+        "file_name": img_file.filename,
+        "prediction": str_pred,
+    }
+    try:
+        json_pred = jsonify(dict_pred)
+    except TypeError as e:
+        json_pred = jsonify({"error": str(e)})
+    logging.info(json_pred)
+    return json_pred
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", debug=True, port=7860)