Update app.py

app.py

@@ -1,20 +1,28 @@
 import gradio as gr
+import tensorflow as tf
+import numpy as np
 from huggingface_hub import from_pretrained_keras
 
 def predict_diagnosis(loops):
-  loops_arr = tf.keras.utils.img_to_array(loops)
-  #new_loops_arr = np.array(loops)
-  new_img = tf.expand_dims(loops_arr,0)
+  if loops.shape[2] == 4:
+      loops = np.delete(loops, 3, axis=2)
+
+  n_img = np.ndarray((1, 255, 906, 3))
+  for i in range(n_img.shape[0]):
+      n_img[i] = tf.keras.utils.img_to_array(tf.image.resize(loops, [255, 906]))
+
+  n_img = n_img.astype(np.float32)/255.0
+
   model = from_pretrained_keras("layai/priya-loop-model")
-  single_data = model.predict(new_img)
+  single_data = model.predict(n_img)
   single_data = single_data[0]
   return np.argmax(single_data)
 
-input = gr.inputs.Image(shape=(906, 255))
+input = gr.inputs.Image()
 output = gr.outputs.Textbox(label='Predicted diagnosis')
 iloops = gr.Interface( fn=predict_diagnosis,
               inputs=input,
               outputs=output,
               capture_session=True)
 
-iloops.launch()
+iloops.launch(debug=True)