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app.py

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+import cv2
+import numpy as np
+from tensorflow.keras.models import load_model
+from flask import Flask, request, jsonify
+from PIL import Image
+from transformers import AutoFeatureExtractor, AutoTokenizer, VisionEncoderDecoderModel
+from flask_cors import CORS
+
+app = Flask(__name__)
+CORS(app)
+# Load the first model - Classfication -
+loaded_model = load_model('C:/files/kolia/GP/ml/model.h5')
+
+# Load the second model and tokenizer   -- Genrate Report --
+encoder_checkpoint = "google/vit-base-patch16-224-in21k"
+decoder_checkpoint = "ahmedabdo/facebook-bart-base-finetuned"
+feature_extractor = AutoFeatureExtractor.from_pretrained(encoder_checkpoint)
+tokenizer = AutoTokenizer.from_pretrained(decoder_checkpoint)
+model = VisionEncoderDecoderModel.from_pretrained(decoder_checkpoint).to('cpu')
+
+def preprocess_image(image_path):
+    image = cv2.imread(image_path)
+    resized_image = cv2.resize(image, (224, 224))
+    input_image = np.expand_dims(resized_image, axis=0)
+    return input_image
+
+def predict_image(image_path):
+    input_image = preprocess_image(image_path)
+    predictions = loaded_model.predict(input_image)
+    return predictions
+
+def get_predicted_label(predictions):
+    int_to_label = {0: 'Arm', 1: 'Chest', 2: 'Knee', 3: 'Vertebrae'}
+    predicted_label = int_to_label[np.argmax(predictions)]
+    return predicted_label
+
+def predict_second_model(image):
+    features = feature_extractor(image, return_tensors="pt").pixel_values.to("cpu")
+    caption = tokenizer.decode(model.generate(features, max_length=1024)[0], skip_special_tokens=True)
+    return caption
+
+# Route to predict image label using the first model
+@app.route('/predict', methods=['POST'])
+def predict():
+    file = request.files['image']
+    if file.filename == '':
+        return jsonify({'error': 'No selected file. Please upload a chest X-ray image.'})
+    image_path = file.filename
+    file.save(image_path)
+    predictions = predict_image(image_path)
+    predicted_label = get_predicted_label(predictions)
+    
+    if predicted_label == 'Chest':
+        img = Image.open(image_path).convert("RGB")
+        caption = predict_second_model(img)
+        return jsonify({'predicted_label': predicted_label,'caption': caption})
+    else:
+        return jsonify({'predicted_label': predicted_label, 'caption': 'Please upload a chest X-ray image.'})
+

requirements.txt

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+Flask==3.0.2
+numpy==1.26.4
+transformers==4.38.2
+Flask-Cors==4.0.0
+Pillow==10.2.0
+gunicorn