app.py

import os
os.environ["GRADIO_SSR_MODE"] = "false"

if not os.path.exists("checkpoints"):
    os.makedirs("checkpoints")
    os.system("pip install gdown")
    os.system("gdown https://drive.google.com/uc?id=1eQe6blJcyI7oy78C8ozwj1IUkbkFEItf; unzip -o dam_3b_v1.zip -d checkpoints")

from segment_anything import sam_model_registry, SamPredictor
import gradio as gr
import numpy as np
import cv2
import base64
import torch
from PIL import Image
import io
import argparse
from fastapi import FastAPI
from fastapi.staticfiles import StaticFiles
from transformers import SamModel, SamProcessor
from dam import DescribeAnythingModel, disable_torch_init
try:
    from spaces import GPU
except ImportError:
    print("Spaces not installed, using dummy GPU decorator")
    GPU = lambda fn: fn

# Load SAM model
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
sam_model = SamModel.from_pretrained("facebook/sam-vit-huge").to(device)
sam_processor = SamProcessor.from_pretrained("facebook/sam-vit-huge")

@GPU(duration=75)
def image_to_sam_embedding(base64_image):
    try:
        # Decode base64 string to bytes
        image_bytes = base64.b64decode(base64_image)
        
        # Convert bytes to PIL Image
        image = Image.open(io.BytesIO(image_bytes))
        
        # Process image with SAM processor
        inputs = sam_processor(image, return_tensors="pt").to(device)
        
        # Get image embedding
        with torch.no_grad():
            image_embedding = sam_model.get_image_embeddings(inputs["pixel_values"])
        
        # Convert to CPU and numpy
        image_embedding = image_embedding.cpu().numpy()
        
        # Encode the embedding as base64
        embedding_bytes = image_embedding.tobytes()
        embedding_base64 = base64.b64encode(embedding_bytes).decode('utf-8')
        
        return embedding_base64
    except Exception as e:
        print(f"Error processing image: {str(e)}")
        raise gr.Error(f"Failed to process image: {str(e)}")

@GPU(duration=75)
def describe(image_base64: str, mask_base64: str, query: str):
    # Convert base64 to PIL Image
    image_bytes = base64.b64decode(image_base64.split(',')[1] if ',' in image_base64 else image_base64)
    img = Image.open(io.BytesIO(image_bytes))
    mask_bytes = base64.b64decode(mask_base64.split(',')[1] if ',' in mask_base64 else mask_base64)
    mask = Image.open(io.BytesIO(mask_bytes))
    
    # Process the mask
    mask = Image.fromarray((np.array(mask.convert('L')) > 0).astype(np.uint8) * 255)
    
    # Get description using DAM with streaming
    description_generator = dam.get_description(img, mask, query, streaming=True)
    
    # Stream the tokens
    text = ""
    for token in description_generator:
        text += token
        yield text

@GPU(duration=75)
def describe_without_streaming(image_base64: str, mask_base64: str, query: str):
    # Convert base64 to PIL Image
    image_bytes = base64.b64decode(image_base64.split(',')[1] if ',' in image_base64 else image_base64)
    img = Image.open(io.BytesIO(image_bytes))
    mask_bytes = base64.b64decode(mask_base64.split(',')[1] if ',' in mask_base64 else mask_base64)
    mask = Image.open(io.BytesIO(mask_bytes))
    
    # Process the mask
    mask = Image.fromarray((np.array(mask.convert('L')) > 0).astype(np.uint8) * 255)
    
    # Get description using DAM
    description = dam.get_description(img, mask, query)
    
    return description

if __name__ == "__main__":
    parser = argparse.ArgumentParser(description="Describe Anything gradio demo")
    parser.add_argument("--model-path", type=str, default="checkpoints/dam_3b_v1", help="Path to the model checkpoint")
    parser.add_argument("--prompt-mode", type=str, default="full+focal_crop", help="Prompt mode")
    parser.add_argument("--conv-mode", type=str, default="v1", help="Conversation mode")
    parser.add_argument("--temperature", type=float, default=0.2, help="Sampling temperature")
    parser.add_argument("--top_p", type=float, default=0.5, help="Top-p for sampling")

    args = parser.parse_args()

    # Initialize DAM model
    disable_torch_init()
    dam = DescribeAnythingModel(
        model_path=args.model_path,
        conv_mode=args.conv_mode,
        prompt_mode=args.prompt_mode,
        temperature=args.temperature,
        top_p=args.top_p,
        num_beams=1,
        max_new_tokens=512,
    ).to(device)

    # Create Gradio interface
    with gr.Blocks() as demo:
        gr.Interface(
            fn=image_to_sam_embedding,
            inputs=gr.Textbox(label="Image Base64"),
            outputs=gr.Textbox(label="Embedding Base64"),
            title="Image Embedding Generator",
            api_name="image_to_sam_embedding"
        )
        gr.Interface(
            fn=describe,
            inputs=[
                gr.Textbox(label="Image Base64"),
                gr.Text(label="Mask Base64"),
                gr.Text(label="Prompt")
            ],
            outputs=[
                gr.Text(label="Description")
            ],
            title="Mask Description Generator",
            api_name="describe"
        )
        gr.Interface(
            fn=describe_without_streaming,
            inputs=[
                gr.Textbox(label="Image Base64"),
                gr.Text(label="Mask Base64"),
                gr.Text(label="Prompt")
            ],
            outputs=[
                gr.Text(label="Description")
            ],
            title="Mask Description Generator (Non-Streaming)",
            api_name="describe_without_streaming"
        )

    demo._block_thread = demo.block_thread
    demo.block_thread = lambda: None
    demo.launch()

    for route in demo.app.routes:
        if route.path == "/":
            demo.app.routes.remove(route)
    demo.app.mount("/", StaticFiles(directory="dist", html=True), name="demo")

    demo._block_thread()