Update app.py

app.py

@@ -490,27 +490,6 @@ plt.show()
 #  return psnr
   
 def result(Input,Choice,Step):
-    if Choice=="Coloring":
-      pre_trained = tf.keras.models.load_model("gradio_pix2pix.h5")
-      size0 = Input.shape[0]
-      size1 = Input.shape[1]
-      start = Input
-      Input = cv2.resize(Input, (256,256), interpolation = cv2.INTER_AREA)
-      Input = cv2.cvtColor(Input , cv2.COLOR_BGR2GRAY)
-      Input = np.array(Input).reshape(1,256,256,1)
-      prediction = pre_trained(Input,training=True)
-      Input = prediction[0]
-      Input = (Input+1)*127.5
-      Input = np.uint8(Input)
-      Input = cv2.resize(Input, (size1,size0), interpolation = cv2.INTER_AREA)
-      finish = Input
-      mse = np.mean((start - finish) ** 2)
-      MAX = np.iinfo(start.dtype).max
-      if mse == 0:
-        Psnr = 100
-      else:
-        Psnr = 20 * math.log10(MAX / math.sqrt(mse))
-      return Input,Psnr
       
     if Choice=="Indoor-Coloring":
       if Step == 1:
@@ -541,7 +520,14 @@ def result(Input,Choice,Step):
       return Input,Psnr
       
     if Choice=="Outdoor-Coloring":
-      pre_trained = tf.keras.models.load_model("gradio_pix2pix.h5")
+    
+      if Step == 1:
+        pre_trained = tf.keras.models.load_model("outdoor_1.h5")
+      if Step == 2:
+        pre_trained = tf.keras.models.load_model("outdoor_2.h5")
+      if Step == 3:
+        pre_trained = tf.keras.models.load_model("outdoor_3.h5")
+        
       size0 = Input.shape[0]
       size1 = Input.shape[1]
       start = Input
@@ -583,6 +569,6 @@ def result(Input,Choice,Step):
 
 import gradio as gr
 
-iface = gr.Interface(fn=result, inputs=[gr.inputs.Image(type="numpy",image_mode="RGB"),gr.inputs.Radio(["Coloring","Indoor-Coloring","Outdoor-Coloring","Enhancement","Repair","Repair and Color"]),gr.inputs.Slider(minimum=1,maximum=3,default=3,step=1)], outputs=[gr.outputs.Image( type="auto", label="Output"),gr.outputs.Textbox(type="number",label="Psnr")],theme="grass",live=True
+iface = gr.Interface(fn=result, inputs=[gr.inputs.Image(type="numpy",image_mode="RGB"),gr.inputs.Radio("Indoor-Coloring","Outdoor-Coloring","Enhancement","Repair","Repair and Color"]),gr.inputs.Slider(minimum=1,maximum=3,default=3,step=1)], outputs=[gr.outputs.Image( type="auto", label="Output"),gr.outputs.Textbox(type="number",label="Psnr")],theme="grass",live=True
 ,css=""" body {background-color: rgba(127,191,63,0.48)} """,title="Image Enhancement",article=""" <a href="https://docs.google.com/document/d/19k6dyR5x_hd1M0yoU8i49dlDWvFmtnBT/edit?usp=sharing&ouid=115743073712072785012&rtpof=true&sd=true" download="example.docx"><img src="https://img.icons8.com/external-itim2101-lineal-color-itim2101/64/000000/external-article-blogger-and-influencer-itim2101-lineal-color-itim2101-1.png" alt="Article"></a>""",examples=[["dog.jpg","Coloring"],["woman.png","Coloring"]]) 
 iface.launch(debug="True",show_tips="True",inbrowser=True)