predict.py

import torch
from torchvision import transforms
from PIL import Image
import json
import numpy as np
# from model import load_model
from transformers import AutoImageProcessor, SwinForImageClassification, ViTForImageClassification
import torch.nn as nn
import os
import pandas as pd
import random
from huggingface_hub import hf_hub_download

# Load labels
with open("labels.json", "r") as f:
    class_names = json.load(f)
print("class_names:", class_names)

class DeiT(nn.Module):
    def __init__(self, model_name="facebook/deit-small-patch16-224", num_classes=None):
        super(DeiT, self).__init__()
        self.model = ViTForImageClassification.from_pretrained(model_name)
        in_features = self.model.classifier.in_features
        self.model.classifier = nn.Sequential(
            nn.Linear(in_features, num_classes)
        )

    def forward(self, images):
        outputs = self.model(images)
        return outputs.logits

# Load model
model_path = hf_hub_download(repo_id="Noha90/AML_16", filename="deit_best_model.pth")
print("Model path:", model_path)
model = DeiT(num_classes=len(class_names))
state_dict = torch.load(model_path, map_location="cpu")
if "model_state_dict" in state_dict:
    state_dict = state_dict["model_state_dict"]
model.load_state_dict(state_dict, strict=False)
model.eval()

#deit transform
transform = transforms.Compose([
    transforms.Resize((224, 224)),
    transforms.ToTensor(),
    transforms.Normalize([0.5, 0.5, 0.5], [0.5, 0.5, 0.5])
])
#Swin
# transform = transforms.Compose([
#     transforms.Resize((224, 224)),
#     transforms.ToTensor(),
#     transforms.Normalize(mean=[0.485, 0.456, 0.406],
#                          std=[0.229, 0.224, 0.225])
# ])


def predict(image_path):
    # Load and prepare image
    image = Image.open(image_path).convert("RGB")
    x = transform(image).unsqueeze(0)

    with torch.no_grad():
        outputs = model(x)
        print("Logits:", outputs.logits)
        probs = torch.nn.functional.softmax(outputs.logits, dim=1)[0]
        print("Probs:", probs)
        print("Sum of probs:", probs.sum())
        top5 = torch.topk(probs, k=5)

    top1_idx = int(top5.indices[0])
    top1_label = class_names[top1_idx]

    # Select a random image from the class subfolder
    class_folder = f"sample_images/{str(top1_label).replace(' ', '_')}"
    reference_image = None
    if os.path.isdir(class_folder):
        # List all image files in the folder
        image_files = [f for f in os.listdir(class_folder) if f.lower().endswith((".jpg", ".jpeg", ".png", ".bmp", ".gif", ".webp"))]
        if image_files:
            chosen_file = random.choice(image_files)
            ref_path = os.path.join(class_folder, chosen_file)
            print(f"[DEBUG] Randomly selected reference image: {ref_path}")
            reference_image = Image.open(ref_path).convert("RGB")
        else:
            print(f"[DEBUG] No images found in {class_folder}")
    else:
        print(f"[DEBUG] Class folder does not exist: {class_folder}")

    # Format Top-5 for gr.Label with class names
    top5_probs = {class_names[int(idx)]: float(score) for idx, score in zip(top5.indices, top5.values)}
    print(f"image path: {image_path}")
    print(f"top1_label: {top1_label}")
    print(f"[DEBUG] Top-5 indices: {top5.indices}")
    print(f"[DEBUG] Top-5 labels: {[class_names[int(idx)] for idx in top5.indices]}")
    print(f"[DEBUG] Top-5 probs: {top5_probs}")

    return image, reference_image, top5_probs