Create app.py

app.py

@@ -0,0 +1,25 @@
+import gradio as gr
+import numpy as np
+import pandas as pd
+import cv2
+import os
+import matplotlib.pyplot as plt
+import tensorflow as tf
+import keras
+import matplotlib
+def dice_loss(y_true, y_pred):
+    smooth = 1.
+    y_true_f = K.flatten(y_true)
+    y_pred_f = K.flatten(y_pred)
+    intersection = y_true_f * y_pred_f
+    score = (2. * K.sum(intersection) + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)
+    return 1. - score
+from keras.models import load_model
+model = load_model('model91.h5',custom_objects={'dice_loss':dice_loss})
+def convert_sketch(img): 
+    img = cv2.resize(img,(178,218))
+    img = img.astype(np.float32)/255.
+    y_pred = model.predict(img.reshape(1,218,178,3))
+    return(y_pred[0])
+custom_text = "hello"
+gr.Interface(fn=convert_sketch,inputs="image",outputs="image",title="Sketch To Image Converter",description="Warning: Upload the sketches of 178*218 pixels to get accurate results.").launch()