app_simplified.py

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()