ml_gen.py

import cv2
import json
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import load_model

model_path = 'static/final_cnn_model.h5'
model = load_model(model_path)
# Generate predictions for a sample image (you can replace this with your own image)

def predict(sample_image_path):
    # sample_image_path = 'static/uploads/input.png'
    sample_image = cv2.imread(sample_image_path)
    sample_image = cv2.resize(sample_image, (224, 224))
    sample_image = sample_image / 255.0  # Normalize pixel values to [0, 1]
    sample_image = np.expand_dims(sample_image, axis=0)  # Add batch dimension
    predictions = model.predict(sample_image)
    all_labels = ["Atelectasis", "Cardiomegaly", "Consolidation", "Edema", "Effusion", "Emphysema",
                "Fibrosis", "Infiltration", "Mass", "Nodule", "Pleural Thickening", "Pneumonia",
                "Pneumothorax"]


    op = {}
    for label, probability in zip(all_labels, predictions[0]):
        op[f'{label}'] = int(probability*10000)/100
        
    op_json = open("static/output.json", "w")
    json.dump(op, op_json, indent=6)
    # Display the class labels and their probabilities for the sample image

if __name__ == "__main__":
    predict()