Add application file

app.py

@@ -0,0 +1,36 @@
+import gradio as gr 
+from PIL import Image 
+import numpy as np
+import tensorflow as tf
+
+class_names = ['Covid19','Normal','Viral Pneumonia']
+
+def process_image(image):
+  image = image.resize((224,224))
+  image = np.array(image) / 255.0
+  image = np.expand_dims(image, axis=0) # (224,224,3)
+  return image
+
+model = tf.keras.models.load_model('Covid19_Model.h5')
+
+def predict_image(image):
+  processed_image = process_image(image)
+  prediction = model.predict(processed_image)
+  predicted_class_index = np.argmax(prediction)
+  predicted_class_name = class_names[predicted_class_index]
+  return f"Prediction: {predicted_class_name}"
+
+examples = ['D:/Machine Learning Course/Covid19_Deployment/Covid19-viral-normal/Samples/064.jpeg',
+            'D:/Machine Learning Course/Covid19_Deployment/Covid19-viral-normal/Samples/072.jpeg',
+            'D:/Machine Learning Course/Covid19_Deployment/Covid19-viral-normal/Samples/076.jpeg']
+
+interface = gr.Interface(fn= predict_image,
+                          inputs= gr.Image(type="pil"),
+                          outputs='text',
+                          title= "Covid19, Viral pneumonia or Normal",
+                          description="import an image to get predictions",
+                          examples=examples)
+
+
+
+interface.launch()