Update app.py

app.py

@@ -245,7 +245,6 @@ def submit_comment(comment):
     elif comment_images[0] in comments:
         comments.pop(comment_images[0], None)
 
-    print(comments)
     next_comment = ""
     if comment_images[0] in comments:
         next_comment = comments[comment_images[0]]
@@ -263,7 +262,6 @@ def next_image():
     comment_images.append(comment_images[0])
     comment_images = comment_images[1:]
 
-    print(comments)
     next_comment = ""
     if comment_images[0] in comments:
         next_comment = comments[comment_images[0]]
@@ -279,7 +277,6 @@ def previous_image():
     comment_images = comment_images[1:]
     comment_images = comment_images[::-1]
 
-    print(comments)
     next_comment = ""
     if comment_images[0] in comments:
         next_comment = comments[comment_images[0]]
@@ -341,6 +338,20 @@ Here are the images and their corresponding comments:
     
     if re.match(r"(.|\n)*Assistant: Liked Art Features: (.|\n)*Disliked Art Features: (.|\n)*", generated_texts):
         positive_vp, negative_vp = re.search('.* \nAssistant: Liked Art Features: (.*)\nDisliked Art Features: (.*)', generated_texts).groups()
+        positive_vp = positive_vp.split(", ")
+        negative_vp = negative_vp.split(", ")
+        common = list(set(positive_vp).intersection(negative_vp))
+
+        for vp in positive_vp:
+            if vp in common:
+                positive_vp.remove(vp)
+
+        for vp in negative_vp:
+            if vp in common:
+                negative_vp.remove(vp)
+
+        positive_vp = ", ".join(positive_vp)
+        negative_vp = ", ".join(negative_vp)
         gr.Info("Visual preference successfully extracted.")
     else:
         positive_vp = ""
@@ -456,7 +467,6 @@ def api_fn(api):
             ]
         )
         gr.Info("Valid API")
-        print("correct")
         valid_api = api
 
     except anthropic.AuthenticationError:
@@ -498,7 +508,6 @@ def generate(prompt, vp_pos, vp_neg, slider, example_prompt, gallery, num_infere
         
     generator = torch.Generator().manual_seed(seed)
     
-    print(f"Prompt: {prompt}")
     image = pipe(prompt=prompt, 
                 num_inference_steps=num_inference_steps, 
                 vp_pos=vp_pos, 
@@ -606,8 +615,8 @@ with gr.Blocks(css=css, title="ViPer Demo", theme=gr.themes.Base()) as demo:
             with gr.Accordion("Examples of Effective Comments", open=False):
                 example_comment_1 = gr.Textbox(
                     label="Example 1",
-                    lines=4,
-                    value="Gotta say I love this one. The idea of collage painting really appeals to me. I can pick up on the subtle shadows. The combination of soft, creamy yellow and warm green looks really nice too. The paper texture itself is really interesting.",
+                    lines=2,
+                    value="I don't like this at all. The beige colors bother me. It's so minimal and boring. The texture feels too shallow.",
                 )
 
                 example_comment_2 = gr.Textbox(
@@ -674,7 +683,7 @@ with gr.Blocks(css=css, title="ViPer Demo", theme=gr.themes.Base()) as demo:
                 Generate personalized images using the visual preference extracted from your comments by entering a prompt below! You can adjust the personalization degree to generate results that are more or less personalized and diverse.
                 """)
 
-            slider = gr.Slider(value=0.85, minimum=0, maximum=1.5, label="Personalization degree", interactive=True)
+            slider = gr.Slider(value=0.85, minimum=0, maximum=1, label="Personalization degree", interactive=True)
     
             with gr.Row():
                 prompt = gr.Dropdown(
@@ -707,7 +716,7 @@ with gr.Blocks(css=css, title="ViPer Demo", theme=gr.themes.Base()) as demo:
                 randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
 
     with gr.Row(elem_id="main-container"):
-        with gr.Accordion("Images generated from the example prompts, but with different extracted preferences. The first image shows the non-personalized baseline generation.", open=False): 
+        with gr.Accordion("Images generated from the example prompts, but with different extracted preferences. The first image shows the non-personalized baseline generation.", open=True): 
             example_prompt = gr.Markdown(f"Prompt: {example_prompts[0]}")
             gallery = gr.Gallery(
                 value=examples[example_prompts[0]],