Create app.py

app.py

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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()