Update app.py

app.py

@@ -1,5 +1,3 @@
-!apt-get install -y chromium-browser chromium-chromedriver libnss3 libxss1 libatk-bridge2.0-0 libgtk-3-0 libgbm-dev
-
 import nest_asyncio
 nest_asyncio.apply()
 
@@ -25,17 +23,15 @@ logging.basicConfig(
 )
 
 # Roboflow and model configuration
-ROBOFLOW_API_KEY = "KUP9w62eUcD5PrrRMJsV"  # Replace with your API key if needed
+ROBOFLOW_API_KEY = "KUP9w62eUcD5PrrRMJsV"
 PROJECT_NAME = "model_verification_project"
 VERSION_NUMBER = 2
 
-# ----------------------------
-# Asynchronous function to generate handwriting image via Pyppeteer
-# ----------------------------
 async def _generate_handwriting_image(text_prompt, screenshot_path):
     try:
         browser = await launch(
             headless=True,
+            # If you installed chromium via apt, you can specify the path:
             executablePath="/usr/bin/chromium-browser",
             args=[
                 '--no-sandbox',
@@ -68,9 +64,6 @@ async def _generate_handwriting_image(text_prompt, screenshot_path):
             await browser.close()
 
 def generate_handwriting_image(text_prompt, screenshot_path="/tmp/handwriting.png"):
-    """
-    Synchronous wrapper around the async Pyppeteer call.
-    """
     try:
         loop = asyncio.get_event_loop()
         result = loop.run_until_complete(_generate_handwriting_image(text_prompt, screenshot_path))
@@ -79,9 +72,6 @@ def generate_handwriting_image(text_prompt, screenshot_path="/tmp/handwriting.pn
         logging.error(f"Error generating handwriting image: {e}")
         return None
 
-# ----------------------------
-# Detect paper angle within bounding box
-# ----------------------------
 def detect_paper_angle(image, bounding_box):
     x1, y1, x2, y2 = bounding_box
     roi = np.array(image)[y1:y2, x1:x2]
@@ -100,9 +90,6 @@ def detect_paper_angle(image, bounding_box):
     else:
         return 0
 
-# ----------------------------
-# Main processing function
-# ----------------------------
 def process_image(image, text):
     try:
         # Initialize Roboflow
@@ -126,30 +113,23 @@ def process_image(image, text):
         pil_image = image.convert("RGBA")
         logging.debug("Converted image to RGBA mode.")
 
-        # Iterate over detected objects (assumed white paper)
         for obj in prediction['predictions']:
-            # Paper dimensions
             white_paper_width = obj['width']
             white_paper_height = obj['height']
-
-            # Padding
             padding_x = int(white_paper_width * 0.1)
             padding_y = int(white_paper_height * 0.1)
             box_width = white_paper_width - 2 * padding_x
             box_height = white_paper_height - 2 * padding_y
             logging.debug(f"Padded white paper dimensions: width={box_width}, height={box_height}.")
 
-            # Calculate padded coordinates
             x1_padded = int(obj['x'] - white_paper_width / 2 + padding_x)
             y1_padded = int(obj['y'] - white_paper_height / 2 + padding_y)
             x2_padded = int(obj['x'] + white_paper_width / 2 - padding_x)
             y2_padded = int(obj['y'] + white_paper_height / 2 - padding_y)
 
-            # Detect paper angle
             angle = detect_paper_angle(np.array(image), (x1_padded, y1_padded, x2_padded, y2_padded))
             logging.debug(f"Detected paper angle: {angle} degrees.")
 
-            # (Optional) debug bounding box
             debug_layer = pil_image.copy()
             debug_draw = ImageDraw.Draw(debug_layer)
             debug_draw.rectangle([(x1_padded, y1_padded), (x2_padded, y2_padded)], outline="red", width=3)
@@ -166,7 +146,6 @@ def process_image(image, text):
             handwriting_img = handwriting_img.resize((box_width, box_height), Image.ANTIALIAS)
             rotated_handwriting = handwriting_img.rotate(-angle, resample=Image.BICUBIC, expand=True)
 
-            # Composite the handwriting
             text_layer = Image.new("RGBA", pil_image.size, (255, 255, 255, 0))
             paste_x = int(obj['x'] - rotated_handwriting.size[0] / 2)
             paste_y = int(obj['y'] - rotated_handwriting.size[1] / 2)
@@ -174,7 +153,6 @@ def process_image(image, text):
             pil_image = Image.alpha_composite(pil_image, text_layer)
             logging.debug("Handwriting layer composited onto the original image.")
 
-        # Save output
         output_image_path = "/tmp/output_image.png"
         pil_image.convert("RGB").save(output_image_path)
         logging.debug(f"Output image saved to {output_image_path}.")
@@ -184,9 +162,6 @@ def process_image(image, text):
         logging.error(f"Error during image processing: {e}")
         return None
 
-# ----------------------------
-# Gradio inference function
-# ----------------------------
 def gradio_inference(image, text):
     logging.debug("Starting Gradio inference.")
     result_path = process_image(image, text)
@@ -196,9 +171,6 @@ def gradio_inference(image, text):
     logging.error("Gradio inference failed.")
     return None, None, "An error occurred while processing the image. Please check the logs."
 
-# ----------------------------
-# Gradio interface
-# ----------------------------
 interface = gr.Interface(
     fn=gradio_inference,
     inputs=[
@@ -215,8 +187,6 @@ interface = gr.Interface(
     allow_flagging="never"
 )
 
-# ... [rest of your existing code] ...
-
 if __name__ == "__main__":
     interface.launch(
         server_name="0.0.0.0",