Upload app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+
+model = tf.keras.models.load_model('model.hdf5')
+
+def predict(img_path):
+    test_image = image.load_img(img_path, target_size=(224,224))
+    test_image = image.img_to_array(test_image)
+    test_image = test_image/255.0
+    test_image = np.expand_dims(test_image, axis = 0) 
+    prediction = model.predict(test_image)
+    result = np.argmax(prediction, axis=1)
+    if result[0] == 0:
+        prediction = 'COVID DETECTED'
+    elif result[0] == 1:
+        prediction = 'HEALTHY'
+    elif result[0] == 2:
+        prediction = 'LUNG CANCER DETECTED'
+    else:
+        prediction = 'PNEUMONIA DETECTED'
+    return prediction
+
+iface = gr.Interface(fn=predict, inputs="text", outputs="text")
+iface.launch(share=True)