add client.predict and documentation

Dockerfile

@@ -6,13 +6,13 @@
 # - Git LFS for large file support.
 # - Required libraries: OpenCV, Hugging Face, Gradio, OpenGL.
 # - Gradio server on port 7861.
-
+#
 # 1. Build the image with CUDA support.
 # Example:
 #   ```bash
 #   sudo nvidia-docker build -t omniparser .
 #   ```
-
+#
 # 2. Run the Docker container with GPU access and port mapping for Gradio.
 # Example:
 #   ```bash

client.py

@@ -1,7 +1,7 @@
 """
-This module provides a command-line interface to interact with the OmniParser Gradio server.
+This module provides a command-line interface and programmatic API to interact with the OmniParser Gradio server.
 
-Usage:
+Command-line usage:
     python client.py "http://<server_ip>:7861" "path/to/image.jpg"
 
 View results:
@@ -11,6 +11,10 @@
         Windows: start output_image_<timestamp>.png
         Linux:   xdg-open output_image_<timestamp>.png
 
+Programmatic usage:
+    from omniparse.client import predict
+    result = predict("http://<server_ip>:7861", "path/to/image.jpg")
+
 Result data format:
     {
         "label_coordinates": {
@@ -33,30 +37,31 @@
 import fire
 from gradio_client import Client
 from loguru import logger
-from PIL import Image
 import base64
-from io import BytesIO
 import os
 import shutil
 import json
 from datetime import datetime
 
-def predict(server_url: str, image_path: str, box_threshold: float = 0.05, iou_threshold: float = 0.1):
+# Define constants for default thresholds
+DEFAULT_BOX_THRESHOLD = 0.05
+DEFAULT_IOU_THRESHOLD = 0.1
+
+def predict(server_url: str, image_path: str, box_threshold: float = DEFAULT_BOX_THRESHOLD, iou_threshold: float = DEFAULT_IOU_THRESHOLD):
     """
     Makes a prediction using the OmniParser Gradio client with the provided server URL and image.
-
     Args:
         server_url (str): The URL of the OmniParser Gradio server.
         image_path (str): Path to the image file to be processed.
         box_threshold (float): Box threshold value (default: 0.05).
         iou_threshold (float): IOU threshold value (default: 0.1).
+    Returns:
+        dict: Parsed result data containing label coordinates and parsed content list.
     """
     client = Client(server_url)
-
-    # Generate a timestamp for unique file naming
-    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
 
     # Load and encode the image
+    image_path = os.path.expanduser(image_path)
     with open(image_path, "rb") as image_file:
         encoded_image = base64.b64encode(image_file.read()).decode("utf-8")
 
@@ -72,47 +77,56 @@ def predict(server_url: str, image_path: str, box_threshold: float = 0.05, iou_t
     }
 
     # Make the prediction
-    try:
-        result = client.predict(
-            image_input,    # image input as dictionary
-            box_threshold,  # box_threshold
-            iou_threshold,  # iou_threshold
-            api_name="/process"
-        )
+    result = client.predict(
+        image_input,
+        box_threshold,
+        iou_threshold,
+        api_name="/process"
+    )
 
-        # Process and log the results
-        output_image, result_json = result
-
-        logger.info("Prediction completed successfully")
+    # Process and return the result
+    output_image, result_json = result
+    result_data = json.loads(result_json)
 
-        # Parse the JSON string into a Python object
-        result_data = json.loads(result_json)
+    return {"output_image": output_image, "result_data": result_data}
 
-        # Extract label_coordinates and parsed_content_list
-        label_coordinates = result_data['label_coordinates']
-        parsed_content_list = result_data['parsed_content_list']
 
-        logger.info(f"{label_coordinates=}")
-        logger.info(f"{parsed_content_list=}")
+def predict_and_save(server_url: str, image_path: str, box_threshold: float = DEFAULT_BOX_THRESHOLD, iou_threshold: float = DEFAULT_IOU_THRESHOLD):
+    """
+    Makes a prediction and saves the results to files, including logs and image outputs.
+    Args:
+        server_url (str): The URL of the OmniParser Gradio server.
+        image_path (str): Path to the image file to be processed.
+        box_threshold (float): Box threshold value (default: 0.05).
+        iou_threshold (float): IOU threshold value (default: 0.1).
+    """
+    # Generate a timestamp for unique file naming
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+    # Call the predict function to get prediction data
+    try:
+        result = predict(server_url, image_path, box_threshold, iou_threshold)
+        output_image = result["output_image"]
+        result_data = result["result_data"]
 
         # Save result data to JSON file
         result_data_path = f"result_data_{timestamp}.json"
         with open(result_data_path, "w") as json_file:
             json.dump(result_data, json_file, indent=4)
         logger.info(f"Parsed content saved to: {result_data_path}")
-        
+
         # Save the output image
         output_image_path = f"output_image_{timestamp}.png"
         if isinstance(output_image, str) and os.path.exists(output_image):
             shutil.copy(output_image, output_image_path)
             logger.info(f"Output image saved to: {output_image_path}")
         else:
             logger.warning(f"Unexpected output_image format or file not found: {output_image}")
-    
+
     except Exception as e:
         logger.error(f"An error occurred: {str(e)}")
         logger.exception("Traceback:")
 
-if __name__ == "__main__":
-    fire.Fire(predict)
 
+if __name__ == "__main__":
+    fire.Fire(predict_and_save)