app.py

import os
import glob
import json
import warnings

warnings.filterwarnings("ignore")

import torch
import torchvision.transforms as T
from PIL import Image
import gradio as gr
from datetime import datetime
from ultralytics import YOLO
import models

print(f"Is CUDA available: {torch.cuda.is_available()}")
# print(f"CUDA device: {torch.cuda.get_device_name(torch.cuda.current_device())}")

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# DINOv2

# Select checkpoint
dinov2_ckpt = ['dinov2_vits14', 'dinov2_vitb14', 'dinov2_vitl14', 'dinov2_vitg14'][1]
dinov2 = torch.hub.load('facebookresearch/dinov2', dinov2_ckpt)

dinov2.to(device)
print()

transform_image = T.Compose([
    T.Resize((224, 224)),
    T.ToTensor(),
    T.Normalize(mean=[0.485, 0.456, 0.406],
                        std=[0.229, 0.224, 0.225])
])

def extract_embedding(image):
    """
    Predict the identity of an image.
    Args:
    image: A PIL Image object.
    Returns:
    A string representing the predicted identity of the image.
    """
    
    # Convert the image to a tensor.
    transformed_img = transform_image(image)[:3].unsqueeze(0).to(device)
    
    # Get the embedding of the image.
    with torch.no_grad():
        embedding = dinov2(transformed_img)
        # print(embedding.shape)
        embedding = embedding[0].cpu().numpy().tolist()
    return {
      "embedding": embedding
    }
    
with open("index_to_species.json", "r") as file:
    index_to_species_data = file.read()
index_to_species = json.loads(index_to_species_data)

num_classes = len(list(index_to_species.keys()))

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load the model
linear_model_name = 'linear_2025-08-12.pt'
classify_model = models.LinearClassifier(input_dim=768, output_dim=num_classes)
classify_model.load_state_dict(torch.load(linear_model_name))
detect_model = YOLO('yolov8m_2023-10-23_best.pt')
k = 5

def detect(image):
    results = detect_model(image, conf=0.1)
    # Get the current time
    current_time = datetime.now()
    # Format the current time as a string
    formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S")
    print(formatted_time)
    try:
        results = results[0].boxes.xyxy[0].cpu().numpy()
        top = int(results[1])
        left = int(results[0])
        width = int(results[2] - results[0])
        height = int(results[3] - results[1])
        return {
            "top": top,
            "left": left,
            "width": width,
            "height": height
        }
    except:
        return {
            "top": 0,
            "left": 0,
            "width": 0,
            "height": 0
        }

def classify(image):
    image_width, image_height = image.size
    detection = detect(image)
    
    if detection["top"] == 0 and detection["left"] == 0 and detection["width"] == 0 and detection["height"] == 0:
        return {}
    # Crop the image
    image = image.crop((detection['left'], detection['top'], detection['left'] + detection['width'], detection['top'] + detection['height']))
    
    # Perform the embedding search
    embedding = extract_embedding(image)
    embedding = embedding['embedding']
    output = classify_model(torch.Tensor(embedding).to(device))
    tops = torch.topk(output, k=k).indices
    scores = torch.softmax(output, dim=0)[tops]

    result = {index_to_species[str(tops[i].item())].replace("_", " "): round(scores[i].item(), 2) for i in range(len(tops))}
    sorted_result = {k: v for k, v in sorted(result.items(), key=lambda item: item[1], reverse=True) if v > 0}
    # Get the current time
    current_time = datetime.now()
    
    # Format the current time as a string
    formatted_time = current_time.strftime("%Y-%m-%d %H:%M:%S")
    
    # Print the formatted time
    print(f"{formatted_time} {sorted_result}")
    return {
        "detection": [detection],
        "classification": [sorted_result],
        "image_width": image_width,
        "image_height": image_height
    }

    
title = "🐢"

gr.Interface(
    fn=classify, 
    inputs=gr.Image(type="pil", label="Input Image"),
    outputs=[gr.JSON()],
    title=title,
).launch()