app.py

import os

os.environ['TRANSFORMERS_CACHE'] = '/data/.cache/transformers'
os.environ['HF_HOME'] = '/data/.cache/huggingface'
os.environ['MPLCONFIGDIR'] = '/data/.cache/matplotlib'

import torch
import torch.nn as nn
import yaml
from torchvision import models, transforms
from PIL import Image
import gradio as gr
from transformers import ConvNextV2ForImageClassification
from typing import Dict, Tuple

MODEL_CHECKPOINTS = {
    "ConvNeXt tiny (Best)": "checkpoints/convnext_v2_tiny_best.pth",
    "EfficientNet-B0": "checkpoints/effnet_b0_best.pth",
    "EfficientNet-B3": "checkpoints/effnet_b3_best.pth",
    "Vision Transformer B-16": "checkpoints/vit_b_16_best.pth"
}
DEFAULT_MODEL_NAME = "ConvNeXt tiny (Best)"

MODELS: Dict[str, Tuple[nn.Module, Dict[int, str]]] = {}
DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")

class HFConvNeXtWrapper(nn.Module):
    def __init__(self, model_name, num_labels):
        super(HFConvNeXtWrapper, self).__init__()
        self.model = ConvNextV2ForImageClassification.from_pretrained(
            model_name, num_labels=num_labels, ignore_mismatched_sizes=True)
    def forward(self, x):
        return self.model(x).logits

def get_model(model_name: str, num_classes: int) -> nn.Module:
    model = None
    if model_name == "efficientnet_b0":
        model = models.efficientnet_b0(weights=None)
        num_ftrs = model.classifier[1].in_features
        model.classifier[1] = nn.Linear(num_ftrs, num_classes)
    elif model_name == "efficientnet_b3":
        model = models.efficientnet_b3(weights=None)
        num_ftrs = model.classifier[1].in_features
        model.classifier[1] = nn.Linear(num_ftrs, num_classes)
    elif model_name == "vit_b_16":
        model = models.vit_b_16(weights=None)
        num_ftrs = model.heads.head.in_features
        model.heads.head = nn.Linear(num_ftrs, num_classes)
    elif "convnextv2" in model_name:
        model = HFConvNeXtWrapper(model_name, num_labels=num_classes)
    else:
        raise ValueError(f"Model '{model_name}' not supported.")
    return model

def load_checkpoint(checkpoint_path: str, device: torch.device) -> Tuple[nn.Module, Dict[int, str]]:
    if not os.path.exists(checkpoint_path):
        raise FileNotFoundError(f"Checkpoint file not found at: {checkpoint_path}")
    checkpoint = torch.load(checkpoint_path, map_location=device)
    model_name_from_ckpt = checkpoint['model_name']
    class_to_idx = checkpoint['class_to_idx']
    model = get_model(model_name_from_ckpt, num_classes=1)
    model.load_state_dict(checkpoint['state_dict'])
    model.to(device)
    model.eval()
    idx_to_class = {v: k for k, v in class_to_idx.items()}
    return model, idx_to_class

print("--- Loading all models into memory ---")
for display_name, ckpt_path in MODEL_CHECKPOINTS.items():
    if os.path.exists(ckpt_path):
        model, idx_to_class = load_checkpoint(ckpt_path, DEVICE)
        MODELS[display_name] = (model, idx_to_class)
        print(f"Loaded '{display_name}' on {DEVICE}.")
    else:
        print(f"WARNING: Checkpoint for '{display_name}' not found. Skipping.")

if not MODELS:
    raise RuntimeError("No models were loaded. Please check your checkpoints directory.")

with open('staging_config.yaml', 'r') as f:
    config = yaml.safe_load(f)
IMG_SIZE = config['data_params']['image_size']
inference_transform = transforms.Compose([
    transforms.Resize((IMG_SIZE, IMG_SIZE)),
    transforms.ToTensor(),
    transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])

def predict(pil_image, model_name: str):
    if pil_image is None: return None
    
    model, idx_to_class = MODELS[model_name]
    pil_image = pil_image.convert("RGB")
    image_tensor = inference_transform(pil_image).unsqueeze(0).to(DEVICE)
    
    with torch.no_grad():
        output = model(image_tensor)
        prob = torch.sigmoid(output).item()
        
    class_0_name = idx_to_class.get(0, "Class 0")
    class_1_name = idx_to_class.get(1, "Class 1")
    return {class_0_name: 1 - prob, class_1_name: prob}

iface = gr.Interface(
    fn=predict,
    inputs=[
        gr.Image(type="pil", label="Upload Image"),
        gr.Dropdown(
            choices=list(MODELS.keys()),
            value=DEFAULT_MODEL_NAME,
            label="Select Model"
        )
    ],
    outputs=gr.Label(num_top_classes=2, label="Predictions"),
    title="Multi-Model Image Classifier",
    description="Upload an image and select a model to see its classification.",
)

iface.launch()