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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+models/efficientnetb0_best/savedmodel/variables/variables.data-00000-of-00001 filter=lfs diff=lfs merge=lfs -text

Dockerfile

@@ -0,0 +1,20 @@
+FROM python:3.10-slim
+
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    libjpeg62-turbo \
+    && rm -rf /var/lib/apt/lists/*
+
+WORKDIR /app
+
+COPY requirements.txt /app/requirements.txt
+RUN pip install --no-cache-dir -r requirements.txt
+
+COPY app_gradio.py /app/app_gradio.py
+COPY models /app/models
+
+EXPOSE 7860
+
+ENV TF_CPP_MIN_LOG_LEVEL=2
+ENV TF_FORCE_GPU_ALLOW_GROWTH=true
+
+CMD ["python", "app_gradio.py"]

app_gradio.py

@@ -0,0 +1,82 @@
+import io, numpy as np, tensorflow as tf
+from PIL import Image, UnidentifiedImageError
+import gradio as gr
+
+SAVEDMODEL_DIR = "/content/drive/MyDrive/Pneumonia_Detection/models/efficientnetb0_best/savedmodel"
+INPUT_SIZE     = (224, 224)
+THRESHOLD      = 0.61
+
+loaded = tf.saved_model.load(SAVEDMODEL_DIR)
+infer  = loaded.signatures["serving_default"]
+
+def _read_dicom_bytes(b: bytes):
+    import pydicom
+    dcm = pydicom.dcmread(io.BytesIO(b), force=True)
+    arr = dcm.pixel_array.astype("float32")
+    photometric = str(getattr(dcm, "PhotometricInterpretation", "")).upper()
+    if "MONOCHROME1" in photometric:
+        arr = arr.max() - arr
+    if arr.max() > 0:
+        arr = arr / arr.max()
+    if arr.ndim == 2:
+        arr = np.stack([arr]*3, axis=-1)
+    elif arr.ndim == 3 and arr.shape[-1] == 1:
+        arr = np.concatenate([arr]*3, axis=-1)
+    return arr
+
+def _read_image_bytes(b: bytes):
+    img = Image.open(io.BytesIO(b)).convert("RGB")
+    return (np.asarray(img).astype("float32") / 255.0)
+
+def _prep_01_rgb(arr):
+    x = tf.image.resize(arr, INPUT_SIZE).numpy().astype("float32")
+    x = np.clip(x, 0.0, 1.0)
+    xb = np.expand_dims(x, 0)
+    return tf.constant(xb), x
+
+def _get_prob(out_dict):
+    t = next(iter(out_dict.values()))
+    v = t.numpy()
+    return float(v.reshape(-1)[0]) if v.shape[-1] == 1 else float(v[0, 1])
+
+def predict(file):
+    if file is None:
+        return None, {"NORMAL": 0.0, "PNEUMONIA": 0.0}, "No file uploaded."
+    raw = file.read()
+    name = getattr(file, "name", "upload")
+    try:
+        if name.lower().endswith((".dcm", ".dicom")):
+            arr = _read_dicom_bytes(raw)
+        else:
+            arr = _read_image_bytes(raw)
+    except Exception:
+        try:
+            arr = _read_image_bytes(raw)
+        except Exception:
+            try:
+                arr = _read_dicom_bytes(raw)
+            except Exception as e:
+                return None, {"NORMAL": 0.0, "PNEUMONIA": 0.0}, f"Unreadable file: {e}"
+
+    xb, x_disp = _prep_01_rgb(arr)
+    out  = infer(xb)
+    prob = _get_prob(out)
+    decision = "PNEUMONIA" if prob >= THRESHOLD else "NORMAL"
+    return x_disp, {"NORMAL": 1 - prob, "PNEUMONIA": prob}, f"Decision @ {THRESHOLD:.2f}: {decision} (p={prob:.3f})"
+
+with gr.Blocks(title="Pneumonia Detector – EfficientNetB0") as demo:
+    gr.Markdown("# Pneumonia Detector – EfficientNetB0")
+    gr.Markdown(
+        "Upload **PNG/JPG** or **DICOM**. Input is resized to **224×224 RGB [0,1]**; "
+        "the SavedModel contains its own preprocessing. Threshold **0.61**."
+    )
+    inp = gr.File(label="Upload chest X-ray (.png, .jpg, .dcm)")
+    with gr.Row():
+        out_img = gr.Image(label="Model Input (224×224)")
+        out_lbl = gr.Label(label="Probabilities")
+    out_txt = gr.Textbox(label="Decision")
+    btn = gr.Button("Predict")
+    btn.click(fn=predict, inputs=[inp], outputs=[out_img, out_lbl, out_txt])
+
+if __name__ == "__main__":
+    demo.launch(Share=True)

models/efficientnetb0_best/savedmodel/fingerprint.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9d92ad8dce5965ff747819adabd1ec0439864e20bf0c5e2491134d66c27a63b5
+size 81

models/efficientnetb0_best/savedmodel/saved_model.pb

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:89e8e3eaf661c9ab0d606b82045c8c74baa32d6e12d654164f37d92bdb36524c
+size 2084046

models/efficientnetb0_best/savedmodel/variables/variables.data-00000-of-00001

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:5b635b2fb60d6c7214b32d3278a7854825badb441a014f923fdb0de1a9d9dba0
+size 192971525

requirements.txt

@@ -0,0 +1,8 @@
+gradio>=4.0.0
+pillow
+numpy
+tensorflow-cpu==2.15.0
+pydicom
+pylibjpeg
+pylibjpeg-libjpeg
+pylibjpeg-openjpeg