Re-Added bad words filter for spam / bot traffic

app.py

@@ -45,7 +45,7 @@ my_token = os.environ['api_key']
 pipe = StableDiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-2", revision="fp16", torch_dtype=torch.float16, use_auth_token=my_token).to("cuda")
 
 def check_prompt(prompt):
-    SPAM_WORDS = [] # phasing this out 
+    SPAM_WORDS = ['Д', 'boob', 'boobs', 'breast', 'breasts'] # phasing this out 
     for spam_word in SPAM_WORDS:
         if spam_word in prompt:
             return False
@@ -375,45 +375,46 @@ with gr.Blocks() as beta:
 
 # ----- Canny Edge Tab -----------------------------------------------------------------
 
-from PIL import Image
+# from PIL import Image
 # import gradio as gr
-import numpy as np
-import cv2
+# import numpy as np
+# import cv2
 
-# Define a function to process the uploaded image
-def canny_process_image(input_image, input_low_threshold, input_high_threshold, input_invert):
-    # Convert the input image to a NumPy array
-    np_image = np.array(input_image)
+# # Define a function to process the uploaded image
+# def canny_process_image(input_image, input_low_threshold, input_high_threshold, input_invert):
+#     # Convert the input image to a NumPy array
+#     np_image = np.array(input_image)
 
-    output_image = input_image  # For example, just return the input image
-    numpy_image = np.array(output_image)
-    # Return the processed image
+#     output_image = input_image  # For example, just return the input image
+#     numpy_image = np.array(output_image)
+#     # Return the processed image
 
-    low_threshold = 100
-    high_threshold = 200
-    canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
-    canny_1 = canny_1[:, :, None]
-    canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
-    if input_invert:
-      canny_1 = 255 - canny_1
-    canny_2 = Image.fromarray(canny_1)
+#     low_threshold = 100
+#     high_threshold = 200
+#     canny_1 = cv2.Canny(numpy_image, input_low_threshold, input_high_threshold)
+#     canny_1 = canny_1[:, :, None]
+#     canny_1 = np.concatenate([canny_1, canny_1, canny_1], axis=2)
+#     if input_invert:
+#       canny_1 = 255 - canny_1
+#     canny_2 = Image.fromarray(canny_1)
 
-    return np.array(canny_2)
+#     return np.array(canny_2)
 
-# Define the input and output interfaces
-canny_input_image = gr.inputs.Image()
-canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
-canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
-canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
+# # Define the input and output interfaces
+# canny_input_image = gr.inputs.Image()
+# canny_input_low_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Lower Threshold:", default=100)
+# canny_input_high_threshold = gr.inputs.Slider(minimum=0, maximum=1000, step=1, label="Upper Threshold:", default=200)
+# canny_input_invert = gr.inputs.Checkbox(label="Invert Image")
 
-canny_outputs = gr.outputs.Image(type="numpy")
+# canny_outputs = gr.outputs.Image(type="numpy")
 
-# Create the Gradio interface
-canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
+# # Create the Gradio interface
+# canny_interface = gr.Interface(fn=canny_process_image, inputs=[canny_input_image, canny_input_low_threshold, canny_input_high_threshold, canny_input_invert], outputs=canny_outputs, title='Canny Edge Tracing', allow_flagging='never')
 
 
 
 # ----- Launch Tabs -----------------------------------------------------------------
 
-tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta, canny_interface], ["Artists", "Advanced", "Beta", "Edges"])
+# tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta, canny_interface], ["Artists", "Advanced", "Beta", "Edges"])
+tabbed_interface = gr.TabbedInterface([new_welcome, advanced_tab, beta], ["Artists", "Advanced", "Beta"])
 tabbed_interface.launch()