app.py

"""
Room Object Segmentation API - Placeholder Version
Returns mock data for frontend development
Real ML inference coming soon with ZeroGPU optimization
"""
import gradio as gr
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import random

def create_placeholder_annotated_image(image_path, num_objects):
    """Create a placeholder annotated image with bounding boxes."""
    img = Image.open(image_path).convert("RGB")
    draw = ImageDraw.Draw(img)
    
    width, height = img.size
    
    # Draw some random boxes
    colors = ['red', 'blue', 'green', 'yellow', 'purple', 'orange']
    for i in range(num_objects):
        x1 = random.randint(0, width - 200)
        y1 = random.randint(0, height - 200)
        x2 = x1 + random.randint(100, 200)
        y2 = y1 + random.randint(100, 200)
        
        color = colors[i % len(colors)]
        draw.rectangle([x1, y1, x2, y2], outline=color, width=3)
        
    return img

def segment(image, text_prompt, box_threshold=0.35, text_threshold=0.25):
    """
    PLACEHOLDER API - Returns mock detection data for frontend development.
    Real Grounding DINO + SAM2 inference will be enabled after ZeroGPU optimization.
    """
    if image is None:
        return None, {"error": "No image provided"}
    
    try:
        # Parse prompt
        objects = [obj.strip() for obj in text_prompt.split('.') if obj.strip()]
        
        # Create mock detections (3-5 random objects from prompt)
        num_detections = min(random.randint(3, 5), len(objects))
        detected_objects = random.sample(objects, num_detections)
        
        # Generate realistic mock data
        detections = []
        for i, obj in enumerate(detected_objects):
            detections.append({
                "label": obj,
                "score": round(random.uniform(0.7, 0.95), 2),
                "sam_score": round(random.uniform(0.85, 0.99), 2),
                "box": [
                    random.randint(50, 300),
                    random.randint(50, 300),
                    random.randint(350, 600),
                    random.randint(350, 600)
                ],
                "area": random.randint(15000, 50000)
            })
        
        # Create placeholder annotated image
        annotated_img = create_placeholder_annotated_image(image, num_detections)
        
        result = {
            "status": "placeholder",
            "message": "🚧 Placeholder response for frontend development. Real ML inference coming soon.",
            "num_detections": num_detections,
            "prompt": text_prompt,
            "box_threshold": box_threshold,
            "text_threshold": text_threshold,
            "detections": detections,
            "cached": False,
            "inference_time_ms": random.randint(50, 150),
            "model_info": {
                "grounding_dino": "IDEA-Research/grounding-dino-tiny (placeholder)",
                "sam2": "facebook/sam2.1-hiera-small (placeholder)"
            }
        }
        
        return annotated_img, result
        
    except Exception as e:
        import traceback
        error_msg = f"Error: {str(e)}\n{traceback.format_exc()}"
        print(error_msg)
        return None, {"error": error_msg}

# Gradio interface
demo = gr.Interface(
    fn=segment,
    inputs=[
        gr.Image(type="filepath", label="Upload room image"),
        gr.Textbox(
            value="chair . table . sofa . lamp . bed . cabinet . shelf . desk",
            label="Objects to detect (dot-separated)"
        ),
        gr.Slider(0, 1, value=0.35, label="Box threshold"),
        gr.Slider(0, 1, value=0.25, label="Text threshold")
    ],
    outputs=[
        gr.Image(label="Annotated Image"),
        gr.JSON(label="Detection Results")
    ],
    title="🏠 Room Object Segmentation API (Placeholder)",
    description="""
    **🚧 PLACEHOLDER VERSION FOR FRONTEND DEVELOPMENT**
    
    This API returns mock detection data so frontend developers can integrate while we optimize ZeroGPU inference.
    
    ✅ **What works now:**
    - API contract matches final version
    - Realistic response structure
    - Random detections from your prompt
    - Placeholder annotated images
    
    🔜 **Coming soon:**
    - Real Grounding DINO + SAM2 inference
    - Accurate object detection
    - Proper segmentation masks
    - GPU-optimized with ZeroGPU
    
    **For Frontend:** Use this to develop the UI/UX. Response structure will remain the same when we enable real ML.
    """,
    examples=[
        ["resources/Images/room.jpg", "chair . table . sofa . lamp", 0.35, 0.25]
    ],
    api_name="segment",
    show_error=True
)

if __name__ == "__main__":
    demo.launch(server_name="0.0.0.0", server_port=7860, show_error=True)