| import gradio as gr | |
| import tensorflow as tf | |
| import cv2 | |
| title = "Covid 19 Prediction App using X-ray Images" | |
| head = ( | |
| "<center>" | |
| "Upload an X-ray image to check for covid19. The app is for research purposes and not clinically authorized" | |
| "</center>" | |
| ) | |
| cnn = tf.keras.models.load_model("cnn_model.h5") | |
| def predict_input_image(img): | |
| img = img.reshape(1, 500, 500, 1) | |
| prediction = cnn.predict(img).tolist()[0] | |
| class_names = ["Covid"] | |
| return {class_names[i]: 1-prediction[i] for i in range(1)} | |
| image = gr.inputs.Image(shape=(500, 500), image_mode='L', invert_colors=False, source="upload") | |
| label = gr.outputs.Label() | |
| iface = gr.Interface(fn=predict_input_image, inputs=image, outputs=label,title=title, description=head) | |
| iface.launch() |