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| 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() |