Update app.py

app.py

@@ -2,17 +2,13 @@ import os, io
 import cv2
 import gradio as gr
 import tensorflow as tf
-import urllib.request
 import numpy as np
 import keras.backend as K
-from transformers import pipeline
-from matplotlib import pyplot as plt
 
+from matplotlib import pyplot as plt
 from PIL import Image
-from matplotlib import cm
 from tensorflow import keras
 
-from matplotlib import cm
 
 resized_shape = (768, 768, 3)
 IMG_SCALING = (1, 1)
@@ -51,7 +47,7 @@ def dice_coef(y_true, y_pred, smooth=1):
 seg_model = keras.models.load_model('seg_unet_model.h5', custom_objects={'Combo_loss': Combo_loss, 'dice_coef': dice_coef})
 
 # inputs = gr.inputs.Image(type="pil", label="Upload an image")
-image_output = gr.outputs.Image(type="pil", label="Output Image")
+# image_output = gr.outputs.Image(type="pil", label="Output Image")
 # outputs = gr.outputs.HTML() #uncomment for single class output 
 
 rows = 1
@@ -70,16 +66,14 @@ def gen_pred(img, model=seg_model):
     pred = np.squeeze(pred, axis=0)
     fig = plt.figure(figsize=(10, 7))
     fig.add_subplot(rows, columns, 1)
-    plt.imshow(pred, interpolation='catrom')
+    # plt.imshow(pred, interpolation='catrom')
+    plt.imshow(pred)
     plt.axis('off')
-    plt.title("Prediction")
     plt.show()
-    # return "UI in developing process ..."
     return fig
 
 title = "<h1 style='text-align: center;'>Semantic Segmentation</h1>"
 description = "Upload an image and get prediction mask"
-# css_code='body{background-image:url("file=wave.mp4");}'
 
 gr.Interface(fn=gen_pred, 
              inputs=[gr.Image(type='pil')],