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# app.py
import os, glob, traceback
import numpy as np
from PIL import Image
import gradio as gr
import tensorflow as tf
# try to use Keras 3 loader if present (many models saved with Keras 3 need this)
KERAS3_AVAILABLE = False
try:
import keras # pip package "keras" (v3.x)
KERAS3_AVAILABLE = int(keras.__version__.split(".")[0]) >= 3
except Exception:
keras = None
HF_MODEL_ID = "Vedag812/xray_cnn"
CLASS_NAMES = ["NORMAL", "PNEUMONIA"]
def load_model():
from huggingface_hub import hf_hub_download
model_path = hf_hub_download(HF_MODEL_ID, filename="xray_cnn.keras")
# Keras 3 path
if KERAS3_AVAILABLE:
os.environ.setdefault("KERAS_BACKEND", "tensorflow")
try:
return keras.saving.load_model(model_path, compile=False, safe_mode=False)
except Exception:
# fall back to tf.keras if that fails for any reason
pass
# tf.keras path
return tf.keras.models.load_model(model_path, compile=False)
def _infer_input_shape(model):
"""returns (H, W, C) with integers if available, else defaults to (150,150,1)"""
shape = None
try:
shape = tuple(model.inputs[0].shape.as_list()) # works on many TF models
except Exception:
try:
shape = tuple(model.input_shape)
except Exception:
pass
if not shape or len(shape) < 4:
return 150, 150, 1
H = int(shape[1]) if shape[1] else 150
W = int(shape[2]) if shape[2] else 150
C = int(shape[3]) if shape[3] else 1
return H, W, C
def preprocess(pil_img: Image.Image, target_hw_c):
H, W, C = target_hw_c
# always start from grayscale so intensity stays consistent
g = pil_img.convert("L").resize((W, H))
g_arr = np.array(g).astype("float32") / 255.0 # (H,W)
if C == 1:
x = np.expand_dims(g_arr, axis=(0, -1)) # (1,H,W,1)
elif C == 3:
x3 = np.stack([g_arr, g_arr, g_arr], axis=-1) # (H,W,3)
x = np.expand_dims(x3, axis=0) # (1,H,W,3)
else:
# unexpected channel count. tile to that count safely
xC = np.repeat(g_arr[..., None], C, axis=-1)
x = np.expand_dims(xC, axis=0)
return x
def predict_fn(pil_img: Image.Image):
try:
model = load_model()
H, W, C = _infer_input_shape(model)
x = preprocess(pil_img, (H, W, C))
preds = model.predict(x, verbose=0)
# handle models that output shape (1,1) or (1,)
prob = float(preds.ravel()[0])
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
except Exception as e:
# show a readable error with a tip
tip = (
"Tip: if this keeps happening, the Space may need keras>=3 to load a model "
"saved with newer Keras. I handled both paths here, but if your model was saved "
"with a very new version, updating the Space deps can help."
)
err_text = "⚠️ Error during prediction:\n\n" + str(e) + "\n\n" + tip
return {"NORMAL": 0.0, "PNEUMONIA": 0.0}, err_text
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]
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")
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()
btn.click(predict_fn, inputs=inp, outputs=[probs, out_text])
inp.change(predict_fn, inputs=inp, outputs=[probs, out_text])
if __name__ == "__main__":
demo.launch()
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