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# app.py
import os, glob
import numpy as np
from PIL import Image
import gradio as gr
import tensorflow as tf
from functools import lru_cache
from huggingface_hub import hf_hub_download
HF_MODEL_ID = "Vedag812/xray_cnn"
CLASS_NAMES = ["NORMAL", "PNEUMONIA"]
@lru_cache(maxsize=1)
def load_model():
model_path = hf_hub_download(repo_id=HF_MODEL_ID, filename="xray_cnn.keras")
model = tf.keras.models.load_model(model_path, compile=False)
return model
def preprocess(pil_img: Image.Image):
img = pil_img.convert("L").resize((150, 150))
arr = np.array(img).astype("float32") / 255.0
arr = np.expand_dims(arr, axis=(0, -1)) # shape (1,150,150,1)
return arr
def predict_fn(pil_img: Image.Image):
model = load_model()
x = preprocess(pil_img)
prob = float(model.predict(x, verbose=0)[0][0]) # sigmoid
pred_idx = int(prob > 0.5)
confidence = prob if pred_idx == 1 else 1 - prob
probs = {CLASS_NAMES[0]: 1 - prob, CLASS_NAMES[1]: prob}
msg = f"Prediction: {CLASS_NAMES[pred_idx]} | Confidence: {confidence*100:.2f}%"
return probs, msg
def list_examples():
files = []
for pattern in ["images/*.jpeg", "images/*.jpg", "images/*.png"]:
files.extend(glob.glob(pattern))
files = sorted(files)
return [[p] for p in files] # gr.Examples expects list of [path]
with gr.Blocks(css="""
.gradio-container {max-width: 980px !important; margin: auto;}
#title {text-align:center;}
.card {border:1px solid #e5e7eb; border-radius:16px; padding:16px;}
""") as demo:
gr.Markdown("<h1 id='title'>Chest X-Ray Classification</h1>")
gr.Markdown("Upload an image or click a sample from the gallery. The model predicts NORMAL or PNEUMONIA.")
with gr.Row():
with gr.Column(scale=2):
inp = gr.Image(type="pil", image_mode="L", label="Upload X-ray")
with gr.Row():
btn = gr.Button("Predict", variant="primary")
clr = gr.ClearButton(components=[inp], value="Clear")
gr.Markdown("### Samples")
gr.Examples(
examples=list_examples(),
inputs=inp,
examples_per_page=12,
)
with gr.Column(scale=1):
probs = gr.Label(num_top_classes=2, label="Class probabilities")
out_text = gr.Markdown()
# Run on click
btn.click(predict_fn, inputs=inp, outputs=[probs, out_text])
# Also auto-run when image changes (from upload or example click)
inp.change(predict_fn, inputs=inp, outputs=[probs, out_text])
if __name__ == "__main__":
demo.launch()
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