using api and gpu

app.py

@@ -3,10 +3,12 @@ import torch
 from transformers import TrOCRProcessor, VisionEncoderDecoderModel
 import subprocess
 import json
+import spaces
 from PIL import Image, ImageDraw
 import os
 import tempfile
 import numpy as np
+import requests
 
 # Dictionary of model names and their corresponding HuggingFace model IDs
 MODEL_OPTIONS = {
@@ -35,19 +37,21 @@ def load_model(model_name):
     return processor, model
 
 def detect_lines(image_path):
+    # API endpoint
+    url = "https://wjbmattingly-kraken-api.hf.space/detect_lines"
     # Run Kraken for line detection
     lines_json_path = "lines.json"
-    kraken_command = f"kraken -i {image_path} {lines_json_path} segment -bl"
-    subprocess.run(kraken_command, shell=True, check=True)
+    # Prepare the file for upload
+    files = {'file': ('ms.jpg', open(image_path, 'rb'), 'image/jpeg')}
 
-    # Load the lines from the JSON file
-    with open(lines_json_path, 'r') as f:
-        lines_data = json.load(f)
+    # Specify the model to use
+    data = {'model_name': 'catmus-medieval.mlmodel'}
 
-    # Clean up temporary file
-    os.unlink(lines_json_path)
+    # Send the POST request
+    response = requests.post(url, files=files, data=data)
+    result = response.json()["result"]["lines"]
 
-    return lines_data['lines']
+    return result
 
 def extract_line_images(image, lines):
     line_images = []
@@ -88,6 +92,7 @@ def visualize_lines(image, lines):
         draw.polygon(polygon, outline="red")
     return output_image
 
+@spaces.GPU
 def transcribe_lines(line_images, model_name):
     processor, model = load_model(model_name)
     
@@ -152,4 +157,4 @@ with gr.Blocks() as iface:
         outputs=[output_image, output_text]
     )
 
-iface.launch()
+iface.launch(debug=True)