Update app.py

app.py

@@ -35,20 +35,50 @@ def text_behind_image(input_image, text, text_color, font_size, text_opacity):
         
         # Determine font size (relative to image size)
         font_size = int(min(width, height) * (font_size / 100))  # Convert percentage to actual size
+        if font_size < 10:  # Ensure font is at least 10 pixels
+            font_size = 10
         
         # Try to load a nice font, fall back to default if not available
         try:
-            font = ImageFont.truetype("arial.ttf", font_size)
+            # Try different common fonts
+            font_paths = [
+                "arial.ttf", 
+                "/usr/share/fonts/truetype/dejavu/DejaVuSans-Bold.ttf",
+                "/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf"
+            ]
+            font = None
+            for font_path in font_paths:
+                try:
+                    font = ImageFont.truetype(font_path, font_size)
+                    break
+                except:
+                    continue
+                    
+            if font is None:
+                font = ImageFont.load_default()
         except:
             font = ImageFont.load_default()
         
+        # Prepare the text - make it ALL CAPS for better visual impact
+        text = text.upper()
+        
+        # Fill the entire background with repeating text pattern
         # Word wrap the text to fit within the image width
         margin = 20
-        wrapper = textwrap.TextWrapper(width=int(width/font_size*2) - 2*margin)
+        max_chars = max(10, int(width / (font_size/2)) - 2*margin)
+        wrapper = textwrap.TextWrapper(width=max_chars)
         word_list = wrapper.wrap(text)
         
+        # If text is too short, repeat it to fill the background
+        if len(word_list) == 1 and len(text) < 10:
+            repeated_text = (text + " ") * 10
+            word_list = []
+            for i in range(0, height, font_size + 5):
+                word_list.append(repeated_text)
+        
         # Calculate text block height
-        text_height = len(word_list) * (font_size + 10)
+        line_spacing = int(font_size * 1.2)
+        text_height = len(word_list) * line_spacing
         
         # Position text in the center
         y_text = (height - text_height) // 2
@@ -56,17 +86,40 @@ def text_behind_image(input_image, text, text_color, font_size, text_opacity):
         # Draw text with specified opacity
         for line in word_list:
             # Get line width
-            line_width = font.getbbox(line)[2] - font.getbbox(line)[0]
+            try:
+                line_width = font.getbbox(line)[2] - font.getbbox(line)[0]
+            except:
+                # Fallback method to estimate width
+                line_width = len(line) * (font_size // 2)
+                
             # Center the line
             x_text = (width - line_width) // 2
             
             # Draw text with specified opacity
             text_color_with_opacity = text_color_rgb + (int(text_opacity * 255),)
             draw.text((x_text, y_text), line, font=font, fill=text_color_with_opacity)
-            y_text += font_size + 10
+            y_text += line_spacing
+        
+        # Ensure the text is very visible by drawing it multiple times with different Y positions
+        if len(word_list) < 5:
+            for offset in [-line_spacing*2, line_spacing*2]:
+                y_text = (height - text_height) // 2 + offset
+                for line in word_list:
+                    try:
+                        line_width = font.getbbox(line)[2] - font.getbbox(line)[0]
+                    except:
+                        line_width = len(line) * (font_size // 2)
+                    x_text = (width - line_width) // 2
+                    draw.text((x_text, y_text), line, font=font, fill=text_color_with_opacity)
+                    y_text += line_spacing
         
         # Remove background from the original image to get the person silhouette
-        person_img = remove(img)
+        # Use the u2net_human_seg model specifically for better human segmentation
+        try:
+            person_img = remove(img, model_name="u2net_human_seg")
+        except:
+            # Fallback to default model if human_seg not available
+            person_img = remove(img)
         
         # Composite the images: text in background, then person on top
         final_image = Image.alpha_composite(background.convert('RGBA'), person_img.convert('RGBA'))
@@ -111,4 +164,4 @@ with gr.Blocks(title="Text Behind Image") as demo:
     gr.Markdown("4. Click 'Generate' to create your image")
 
 # Launch the app
-demo.launch()
+demo.launch()