import os import io import json import base64 import random import gradio as gr import numpy as np from PIL import Image, ImageDraw, ImageFont from typing import Dict, Any, List # Don't import torch to avoid potential issues # import torch # Simplified OmniParser API that doesn't rely on the actual OmniParser repository # This is a fallback in case the main app.py has issues with dependencies def process_image(image): """ Simplified implementation that simulates OmniParser functionality """ if image is None: return { "error": "No image provided", "elements": [], "visualization": None } # Create a copy of the image for visualization vis_img = image.copy() draw = ImageDraw.Draw(vis_img) # Define some mock UI element types element_types = ["Button", "Text Field", "Checkbox", "Dropdown", "Menu Item", "Icon", "Link"] # ENHANCEMENT OPPORTUNITY: Data Fusion # In a real implementation, we would integrate multiple models: # 1. YOLO for initial detection of UI elements # 2. OCR for text detection # 3. VLM for captioning and context understanding # 4. SAM for precise segmentation # # Example architecture: # ``` # def integrated_detection(image): # # 1. Run YOLO to detect UI elements # yolo_boxes = yolo_model(image) # # # 2. Run OCR to detect text # ocr_results = ocr_model(image) # # # 3. Use VLM to understand the overall context # context = vlm_model.analyze_image(image) # # # 4. For each detected element, use SAM for precise segmentation # elements = [] # for box in yolo_boxes: # # Get SAM mask # mask = sam_model.segment(image, box) # # # Find overlapping text from OCR # element_text = find_overlapping_text(box, ocr_results) # # # Use VLM to caption the element with context # caption = vlm_model.caption_region(image, box, context) # # elements.append({ # "box": box, # "mask": mask, # "text": element_text, # "caption": caption # }) # # return elements # ``` # Generate some random elements elements = [] num_elements = min(15, int(image.width * image.height / 40000)) # Scale with image size for i in range(num_elements): # Generate random position and size x1 = random.randint(0, image.width - 100) y1 = random.randint(0, image.height - 50) width = random.randint(50, 200) height = random.randint(30, 80) x2 = min(x1 + width, image.width) y2 = min(y1 + height, image.height) # Generate random element type and caption element_type = random.choice(element_types) captions = { "Button": ["Submit", "Cancel", "OK", "Apply", "Save"], "Text Field": ["Enter text", "Username", "Password", "Search", "Email"], "Checkbox": ["Select option", "Enable feature", "Remember me", "Agree to terms"], "Dropdown": ["Select item", "Choose option", "Select country", "Language"], "Menu Item": ["File", "Edit", "View", "Help", "Tools", "Settings"], "Icon": ["Home", "Settings", "Profile", "Notification", "Search"], "Link": ["Learn more", "Click here", "Details", "Documentation", "Help"] } text = random.choice(captions[element_type]) caption = f"{element_type}: {text}" # ENHANCEMENT OPPORTUNITY: Confidence Scoring # In a real implementation, confidence scores would be calculated based on: # 1. Detection confidence from YOLO # 2. Text recognition confidence from OCR # 3. Caption confidence from VLM # 4. Segmentation confidence from SAM # # Example implementation: # ``` # def calculate_confidence(detection_conf, ocr_conf, vlm_conf, sam_conf): # # Weighted average of confidence scores # weights = { # "detection": 0.4, # "ocr": 0.2, # "vlm": 0.3, # "sam": 0.1 # } # # confidence = ( # weights["detection"] * detection_conf + # weights["ocr"] * ocr_conf + # weights["vlm"] * vlm_conf + # weights["sam"] * sam_conf # ) # # return confidence # ``` # Add to elements list elements.append({ "id": i, "text": text, "caption": caption, "coordinates": [x1/image.width, y1/image.height, x2/image.width, y2/image.height], "is_interactable": element_type in ["Button", "Checkbox", "Dropdown", "Link", "Text Field"], "confidence": random.uniform(0.7, 0.95) }) # Draw on visualization draw.rectangle([x1, y1, x2, y2], outline="red", width=2) draw.text((x1, y1 - 10), f"{i}: {text}", fill="red") # ENHANCEMENT OPPORTUNITY: Predictive Monitoring # In a real implementation, we would verify the detected elements: # 1. Check if the detected elements make sense in the UI context # 2. Verify that interactive elements have appropriate labels # 3. Ensure that the UI structure is coherent # # Example implementation: # ``` # def verify_ui_elements(elements, image): # # Use VLM to analyze the entire UI # ui_analysis = vlm_model.analyze_ui(image) # # # Check if detected elements match the expected UI structure # verified_elements = [] # for element in elements: # # Verify element type based on appearance and context # verified_type = verify_element_type(element, ui_analysis) # # # Verify interactability # verified_interactable = verify_interactability(element, verified_type) # # verified_elements.append({ # **element, # "verified_type": verified_type, # "verified_interactable": verified_interactable # }) # # return verified_elements # ``` return { "elements": elements, "visualization": vis_img, "note": "This is a simplified implementation that simulates OmniParser functionality. For a real implementation, consider integrating YOLO, VLM, OCR, and SAM models as described in the code comments." } # API endpoint function def api_endpoint(image): """ API endpoint that accepts an image and returns parsed elements Args: image: Uploaded image file Returns: JSON with parsed elements """ if image is None: return json.dumps({"error": "No image provided"}) try: # Process the image result = process_image(image) # Check if there was an error if "error" in result: return json.dumps({ "status": "error", "error": result["error"], "elements": [] }) # Convert visualization to base64 for JSON response buffered = io.BytesIO() result["visualization"].save(buffered, format="PNG") img_str = base64.b64encode(buffered.getvalue()).decode() # Create response response = { "status": "success", "note": result.get("note", ""), "elements": result["elements"], "visualization": img_str } return json.dumps(response) except Exception as e: print(f"API endpoint error: {str(e)}") return json.dumps({ "status": "error", "error": f"API processing error: {str(e)}", "elements": [] }) # Function to handle UI submission def handle_submission(image): """Handle UI submission and provide appropriate feedback""" if image is None: return {"error": "No image provided"}, None # Process the image result = process_image(image) # Return the result if "error" in result: return {"error": result["error"]}, result.get("visualization", None) else: return { "note": result.get("note", ""), "elements": result["elements"] }, result["visualization"] # Create test image if it doesn't exist def create_test_ui_image(): """Create a simple test UI image with buttons and text""" # Create a new image with white background width, height = 800, 600 image = Image.new('RGB', (width, height), color='white') draw = ImageDraw.Draw(image) # Try to load a font, use default if not available try: font = ImageFont.truetype("arial.ttf", 20) small_font = ImageFont.truetype("arial.ttf", 16) except IOError: font = ImageFont.load_default() small_font = ImageFont.load_default() # Draw a header draw.rectangle([(0, 0), (width, 60)], fill='#4285F4') draw.text((20, 15), "Test UI Application", fill='white', font=font) # Draw a sidebar draw.rectangle([(0, 60), (200, height)], fill='#F1F1F1') # Draw menu items in sidebar menu_items = ["Home", "Profile", "Settings", "Help", "Logout"] for i, item in enumerate(menu_items): y = 100 + i * 50 # Highlight one item if item == "Settings": draw.rectangle([(10, y-10), (190, y+30)], fill='#E1E1E1') draw.text((20, y), item, fill='black', font=font) # Draw main content area draw.text((220, 80), "Welcome to the Test UI", fill='black', font=font) # Draw a form draw.text((220, 150), "Please enter your information:", fill='black', font=font) # Draw form fields fields = ["Name", "Email", "Phone"] for i, field in enumerate(fields): y = 200 + i * 60 draw.text((220, y), f"{field}:", fill='black', font=font) draw.rectangle([(320, y-5), (700, y+25)], outline='black') # Draw buttons draw.rectangle([(220, 400), (320, 440)], fill='#4285F4') draw.text((240, 410), "Submit", fill='white', font=font) draw.rectangle([(340, 400), (440, 440)], fill='#9E9E9E') draw.text((360, 410), "Cancel", fill='white', font=font) # Draw a checkbox draw.rectangle([(220, 470), (240, 490)], outline='black') draw.text((250, 470), "Remember me", fill='black', font=small_font) # Save the image os.makedirs("static", exist_ok=True) image_path = "static/test_ui.png" image.save(image_path) print(f"Test UI image created at {image_path}") return image_path # Create test image if it doesn't exist try: if not os.path.exists("static/test_ui.png"): print("Creating test UI image...") test_image_path = create_test_ui_image() print(f"Test image created at {test_image_path}") except Exception as e: print(f"Error creating test image: {str(e)}") # Create Gradio interface with gr.Blocks() as demo: gr.Markdown(""" # OmniParser v2.0 API (Simplified Version) Upload an image to parse UI elements and get structured data. ## Quick Start You can use the [test UI image](/file=static/test_ui.png) to try out the API, or upload your own UI screenshot. ## API Usage You can use this API by sending a POST request with a file upload to this URL. ```python import requests # Replace with your actual API URL after deployment OMNIPARSER_API_URL = "https://your-username-omniparser-api.hf.space/api/parse" # Upload a file files = {'image': open('screenshot.png', 'rb')} # Send request response = requests.post(OMNIPARSER_API_URL, files=files) # Get JSON result result = response.json() ``` ## Note This is a simplified version that simulates OmniParser functionality. It does not use the actual OmniParser models. """) with gr.Row(): with gr.Column(): image_input = gr.Image(type='pil', label='Upload image') # Function to load test image def load_test_image(): if os.path.exists("static/test_ui.png"): return Image.open("static/test_ui.png") return None test_image_button = gr.Button(value='Load Test Image') test_image_button.click(fn=load_test_image, inputs=[], outputs=[image_input]) submit_button = gr.Button(value='Parse Image', variant='primary') # Status message status = gr.Markdown("⚠️ OmniParser v2.0 API - Running in simplified mode (without actual models)") with gr.Column(): json_output = gr.JSON(label='Parsed Elements (JSON)') image_output = gr.Image(type='pil', label='Visualization') # Connect the interface submit_button.click( fn=handle_submission, inputs=[image_input], outputs=[json_output, image_output], api_name="parse" # This creates the /api/parse endpoint ) # Launch the app demo.launch()