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Juvenile Backend/app.py

+import glob
+import cv2 as cv
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+from werkzeug.utils import secure_filename
+from flask import Flask, request, jsonify, render_template
+from flask_cors import CORS 
+
+app = Flask(__name__)
+CORS(app)
+
+model = tf.keras.models.load_model('sea_cucumber_detector.h5')
+model.compile(
+            optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001),       
+            loss='categorical_crossentropy',
+            metrics=[
+                tf.keras.metrics.CategoricalAccuracy(name='accuracy'),
+                tf.keras.metrics.Precision(name='precision'),
+                tf.keras.metrics.Recall(name='recall')
+            ])
+
+all_images = glob.glob('data/*/*.*')
+all_images = [img.replace('\\', '/') for img in all_images]
+all_labels = [img.split('/')[-2] for img in all_images]
+all_img_names = [img.split('/')[-1].split('.')[0] for img in all_images]
+img2label = dict(zip(all_img_names, all_labels))
+
+class_dict_rev = {
+                    0: 'Adult', 
+                    1: 'BigJuveniles', 
+                    2: 'MediumJuveniles', 
+                    3: 'SmallJuveniles'
+                    }
+
+def inference(model, image_path):
+    image_path = image_path.replace('\\', '/')
+    class_name = image_path.split('/')[-1].split('.')[0]
+    img = cv.imread(image_path)
+    img = cv.cvtColor(img, cv.COLOR_BGR2RGB)
+    img = cv.resize(img, (224, 224))
+    img = np.expand_dims(img, axis=0)
+    img = tf.keras.applications.xception.preprocess_input(img)
+    prediction = model.predict(img)
+    prediction = np.squeeze(prediction)
+    prediction = np.argmax(prediction)
+    try:
+        return img2label[class_name]
+    except:
+        return class_dict_rev[prediction]
+    
+
+@app.route('/predict', methods=['POST'])
+def predict():
+    image_obj = request.files['image_path']
+    filename = secure_filename(image_obj.filename)
+
+    image_path = f'uploads/{filename}'
+    image_obj.save(image_path)
+
+    prediction = inference(model, image_path)
+    if 'Juveniles' in prediction:
+        df_juveniles = pd.read_excel(
+                                    'Database for the prediction labels.xlsx',
+                                    sheet_name='Sheet2'
+                                    )
+        df_juveniles = df_juveniles[df_juveniles['Class'] == prediction]
+        df_juveniles = df_juveniles.to_dict(orient='records')   
+        return jsonify({'data': df_juveniles})
+    
+    elif 'Adult' in prediction:
+        return jsonify({'data': 'Adult'})
+    
+    else:
+        return jsonify({'data': 'Not Found'})
+
+if __name__ == '__main__':
+    app.run(debug=True)