add model code

app.py

@@ -1,4 +1,78 @@
 import gradio as gr
+import numpy as np
+import cv2
+import tensorflow as tf
+import keras
+from keras import layers, models
+
+model = model = tf.keras.models.load_model('model/ocr_model.h5')
+
+def preprocessImage(img, shape):
+    img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    img = cv2.resize(img, (shape))
+    img = (img/255).astype(np.float32)
+    img = img.T
+    img = np.expand_dims(img, axis=-1)
+    return img
+
+label2char ={0: ' ',
+ 1: "'",
+ 2: '-',
+ 3: 'A',
+ 4: 'B',
+ 5: 'C',
+ 6: 'D',
+ 7: 'E',
+ 8: 'F',
+ 9: 'G',
+ 10: 'H',
+ 11: 'I',
+ 12: 'J',
+ 13: 'K',
+ 14: 'L',
+ 15: 'M',
+ 16: 'N',
+ 17: 'O',
+ 18: 'P',
+ 19: 'Q',
+ 20: 'R',
+ 21: 'S',
+ 22: 'T',
+ 23: 'U',
+ 24: 'V',
+ 25: 'W',
+ 26: 'X',
+ 27: 'Y',
+ 28: 'Z',
+ 29: '`'}
+
+def getStringFromEncode(lst :list):
+    return ''.join([label2char[i] if i in label2char else '' for i in lst])
+
+def decode_batch_predictions(pred):
+    pred = pred[:, :-2] # first two layers of ctc garbage
+    input_len = np.ones(pred.shape[0])*pred.shape[1]
+    
+    results = keras.backend.ctc_decode(pred, 
+                                        input_length=input_len,
+                                        greedy=True)[0][0]
+    
+    output_text = []
+    for res in results.numpy():
+        outstr = getStringFromEncode(res)
+        output_text.append(outstr)
+    
+    # return final text results
+    return output_text
+
+def predict(img):
+    img = preprocessImage(img, (256,64))
+    img = np.expand_dims(img, axis=0) # 1 image in batch
+
+    preds = model.predict(img)
+    pred_texts = decode_batch_predictions(preds)
+    return pred_texts[0]
+
 
 def greet(name):
     return "Hello " + name + "!!"