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# app.py (Use this code for Hugging Face)
import torch
import gradio as gr
from PIL import Image
from transformers import SwinForImageClassification, ViTImageProcessor
# --- 1. Load Model & Processor ---
MODEL_NAME = "microsoft/swin-tiny-patch4-window7-224"
MODEL_PATH = "best_model_swin.pth"
NUM_CLASSES = 3
CLASS_NAMES = ['COVID19', 'NORMAL', 'PNEUMONIA']
device = torch.device("cpu") # Use CPU for free-tier hosting
processor = ViTImageProcessor.from_pretrained(MODEL_NAME)
model = SwinForImageClassification.from_pretrained(
MODEL_NAME,
num_labels=NUM_CLASSES,
ignore_mismatched_sizes=True
)
model.load_state_dict(torch.load(MODEL_PATH, map_location=device))
model.to(device)
model.eval()
# --- 2. Define Prediction Function ---
def classify_image(input_image: Image.Image):
if input_image is None:
return "Please upload an image."
if input_image.mode != "RGB":
input_image = input_image.convert("RGB")
inputs = processor(images=input_image, return_tensors="pt")
pixel_values = inputs['pixel_values'].to(device)
with torch.no_grad():
outputs = model(pixel_values)
probabilities = torch.nn.functional.softmax(outputs.logits, dim=1)
# Create a dictionary of {class_name: probability}
confidences = {CLASS_NAMES[i]: prob.item() for i, prob in enumerate(probabilities[0])}
return confidences
# --- 3. Create the Gradio Interface ---
iface = gr.Interface(
fn=classify_image,
inputs=gr.Image(type="pil", label="Upload Chest X-Ray"),
outputs=gr.Label(num_top_classes=3, label="Predictions"),
title="Swin Transformer Chest X-Ray Classifier",
description="Upload an X-ray image to classify it as COVID-19, Normal, or Pneumonia."
)
# --- 4. Launch the app ---
iface.launch() |