Update app.py

app.py

@@ -1,5 +1,4 @@
 import os
-#os.chdir("/home/user/app/segment-anything-2")
 import urllib.request
 
 model_urls = {
@@ -17,17 +16,11 @@ def download_models():
         else:
             print(f"{filename} already exists, skipping download.")
 
-# Call it at startup
 download_models()
 
-"""wget -O sam2_hiera_tiny.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_tiny.pt"
-wget -O sam2_hiera_small.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_small.pt"
-wget -O sam2_hiera_base_plus.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_base_plus.pt"
-wget -O sam2_hiera_large.pt "https://dl.fbaipublicfiles.com/segment_anything_2/072824/sam2_hiera_large.pt"
-"""
-
-#import os
 import gradio as gr
+from gradio.themes.base import Base
+from gradio.themes.utils import colors
 import numpy as np
 import pandas as pd
 import cv2
@@ -58,24 +51,15 @@ from PyPDF2 import PdfReader
 from openai import OpenAI
 from IPython.display import display, Markdown, Latex, HTML
 from transformers import GPT2Tokenizer
-#from google.colab import files  # for uploading files
-from termcolor import colored  # for colored text output
-
-#%matplotlib inline
-#%config InlineBackend.figure_format='retina'
-
-#os.chdir("segment-anything-2")
+from termcolor import colored
 
 from sam2.build_sam import build_sam2
 from sam2.sam2_image_predictor import SAM2ImagePredictor
-
 sam2_checkpoint = "sam2_hiera_small.pt"
 model_cfg = "sam2_hiera_s.yaml"
-
 device = "cuda" if torch.cuda.is_available() else "cpu"
 sam2_model = build_sam2(model_cfg, sam2_checkpoint, device=device)
 predictor = SAM2ImagePredictor(sam2_model)
-
 checkpoint_path = "sam2_lr0.0001_wd0.01_900.torch"
 predictor.model.load_state_dict(torch.load(checkpoint_path, map_location=torch.device('cpu')))
 
@@ -107,9 +91,7 @@ def read_pdf(filepath, max_pages=None):
             break
         page_text = page.extract_text()
 
-        # Check if page_text is None before proceeding
         if page_text:
-            # Replace multiple newlines with a space to make it readable
             page_text = re.sub(r'\n+', ' ', page_text)
             pdf_text += page_text + f"\nPage Number: {page_number}\n"
         else:
@@ -144,7 +126,7 @@ tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
 tokenizer.model_max_length = int(1e30)
 
 def ask_chatbot(question, context, m):
-    max_context_tokens = 16385  # Adjust based on the maximum allowable context tokens
+    max_context_tokens = 16385
     truncated_context = truncate_context(context, max_context_tokens)
     response = client.chat.completions.create(
         model=m,
@@ -232,7 +214,7 @@ def one_step_inference(image_path, threshold=0.5):
             sparse_prompt_embeddings=sparse_embeddings,
             dense_prompt_embeddings=dense_embeddings,
             multimask_output=False,
-            repeat_image=False,  # Fixed argument
+            repeat_image=False,
             high_res_features=high_res_features,)
 
     mask = predictor._transforms.postprocess_masks(low_res_masks, predictor._orig_hw[-1])
@@ -245,9 +227,7 @@ def one_step_inference(image_path, threshold=0.5):
 
     return colors["Red"], colors["Orange"], colors["Yellow"], colors["Magenta"], colors["White"], colors["Gray"], colors["Black"]
 
-# Replace this function in your original code
 def classify_colors(hsv_pixels):
-    # Define color ranges in HSV
     color_ranges = {
         'Red': [(0, 50, 50), (10, 255, 255)],  # Red wraps around
         'Red2': [(170, 50, 50), (179, 255, 255)],
@@ -262,17 +242,13 @@ def classify_colors(hsv_pixels):
         'Gray': [(0, 0, 50), (179, 50, 200)],    # Low saturation, varying brightness
         'Black': [(0, 0, 0), (179, 50, 50)]      # Low brightness
     }
-    # Flatten the HSV pixels to process as a single list
     hsv_pixels = hsv_pixels.reshape(-1, 3)
-    # Initialize counts for each color
     color_counts = {color: 0 for color in color_ranges}
-    # Total number of pixels
     total_pixels = hsv_pixels.shape[0]
-    # Classify each pixel
     for pixel in hsv_pixels:
         h, s, v = pixel
         for color, ranges in color_ranges.items():
-            if isinstance(ranges[0], tuple):  # Handles multiple ranges (e.g., red)
+            if isinstance(ranges[0], tuple):
                 lower = ranges[0]
                 upper = ranges[1]
                 if (lower[0] <= h <= upper[0] or lower[0] > upper[0] and (h >= lower[0] or h <= upper[0])) \
@@ -284,7 +260,7 @@ def classify_colors(hsv_pixels):
                 if lower[0] <= h <= upper[0] and lower[1] <= s <= upper[1] and lower[2] <= v <= upper[2]:
                     color_counts[color] += 1
                     break
-    # Calculate percentages
+
     color_counts["Red"] += color_counts["Red2"]
     del color_counts["Red2"]
     if(total_pixels == 0):
@@ -304,7 +280,6 @@ def reveal_group():
 def hide_group():
     return gr.update(visible=False)
 
-# Add new wound to the list
 def add_wound(image, partOfBody):
     wounds.append({"image": image, "description": partOfBody})
     return image, partOfBody
@@ -314,8 +289,47 @@ def clear_inputs(image, partOfBody):
     partOfBody=""
     return image, partOfBody
 
-# Initialize Gradio app
-with gr.Blocks(theme=gr.themes.Glass(dark_mode=True)) as demo:
+class CustomDarkGlass(Base):
+    def __init__(self):
+        super().__init__(
+            primary_hue=colors.blue,
+            secondary_hue=colors.slate,
+            neutral_hue=colors.gray,
+            font=[Base.fonts.SANS],
+        )
+        self.set(
+            # Backgrounds
+            body_background_fill="#1f2937",        # dark slate / near-black
+            body_text_color="#ffffff",             # white text
+            block_background_fill="#374151",       # medium dark gray
+            block_border_color="#4b5563",          # border gray
+            block_shadow=None,
+            block_title_text_color="#ffffff",
+
+            # Input components
+            input_background_fill="#4b5563",        # slightly lighter
+            input_border_color="#6b7280",           # input border gray
+            input_text_color="#ffffff",             # white input text
+
+            # Buttons
+            button_primary_background_fill="#2563eb",  # blue-600
+            button_primary_text_color="#ffffff",
+            button_secondary_background_fill="#4b5563",
+            button_secondary_text_color="#ffffff",
+
+            # Checkbox / Radio
+            checkbox_label_text_color="#ffffff",
+            checkbox_background_color_selected="#2563eb",
+            checkbox_border_color="#6b7280",
+
+            # Slider / Progress
+            slider_color="#2563eb",
+            progress_color="#2563eb",
+        )
+
+theme = CustomDarkGlass()
+
+with gr.Blocks(theme=theme) as demo:
     gr.Markdown("<center><h1>Welcome to WoundView!</h1></center>")
 
     # Sign-up Group