app.py

import torch
import timm
import gradio as gr
import cv2
import json
import numpy as np
from torchvision import transforms
from huggingface_hub import hf_hub_download

# ---------------- CONFIG ---------------- #
MODEL_REPO = "vijeshkp/vit_deit_finetune"
MODEL_FILE = "pytorch_model.bin"
LABEL_FILE = "labels.json"
IMG_SIZE = 224
DEVICE = "cpu"

# ---------------- LOAD LABELS ---------------- #
labels_path = hf_hub_download(MODEL_REPO, LABEL_FILE)
with open(labels_path, "r") as f:
    labels = json.load(f)
class_names = [labels[str(i)] for i in range(len(labels))]

# ---------------- LOAD MODEL ---------------- #
model_path = hf_hub_download(MODEL_REPO, MODEL_FILE)

model = timm.create_model(
    "deit_base_patch16_224",
    pretrained=False,
    num_classes=len(class_names)
)
model.load_state_dict(torch.load(model_path, map_location=DEVICE))
model.eval()

# ---------------- TRANSFORM ---------------- #
transform = transforms.Compose([
    transforms.ToPILImage(),
    transforms.Resize((IMG_SIZE, IMG_SIZE)),
    transforms.ToTensor(),
    transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
])

# ---------------- PREDICTION FUNCTION ---------------- #
def predict(image):
    img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
    tensor = transform(img).unsqueeze(0)

    with torch.no_grad():
        logits = model(tensor)
        probs = torch.softmax(logits, dim=1)[0]

    pred_idx = torch.argmax(probs).item()
    return {
        class_names[i]: float(probs[i])
        for i in range(len(class_names))
    }

# ---------------- GRADIO UI ---------------- #
demo = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="numpy", label="Upload Image"),
    outputs=gr.Label(num_top_classes=2, label="Prediction"),
    title="DeiT Sitting vs Standing Classifier",
    description="Upload a human image to classify posture using a fine-tuned DeiT model."
)

demo.launch()