initial commit

app.py

@@ -0,0 +1,40 @@
+##importing the libraries
+import numpy as np
+import pandas as pd
+from PIL import Image
+import cv2
+import tensorflow as tf
+import os 
+import matplotlib.pyplot as plt
+%matplotlib inline
+from tensorflow.keras.models import load_model
+import gradio as gr
+
+
+# Load your trained model
+model = load_model('tb_pretrained.h5')
+
+### Preprocess the new image
+
+def predict_image(test_image):
+    # img = cv2.imread(test_image)
+    img = np.array(test_image)
+    
+    image_1 = tf.image.resize(img, (256,256))
+
+    image_processed = np.expand_dims(image_1/256, 0)
+    
+    ##prediction
+    
+    yhat = model.predict(image_processed)
+    
+    ## setting a threshold 
+    if yhat[0][1] > 0.70:
+        return (f'There is {round((yhat[0][1])*100,2)}% chance of the image being normal')
+    elif yhat[0][0] > 0.9:
+        return (f'There is {round((yhat[0][0])*100,2)}% chance of an abnormality either than TB being present')
+    else:
+        return (f'There is a chance of TB being present')
+
+
+gr.Interface(predict_image, "image", "label").launch(debug=True, share=True)