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46a2006 ce1d5b2 46a2006 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 | import streamlit as st
import numpy as np
import tensorflow as tf
import pydicom
from PIL import Image
import io
@st.cache_resource
def load_model():
return tf.keras.models.load_model("best_cnn_model.h5")
model = load_model()
IMG_SIZE = 128
st.title("Pneumonia Detection from Chest X-ray")
st.write("Upload a chest X-ray image (DICOM format)")
uploaded_file = st.file_uploader("Upload DICOM Image", type=None)
def preprocess_dicom_bytes(dicom_bytes):
dicom = pydicom.dcmread(io.BytesIO(dicom_bytes))
img = dicom.pixel_array.astype("float32")
img = img / np.max(img)
img = Image.fromarray((img * 255).astype(np.uint8))
img = img.resize((IMG_SIZE, IMG_SIZE))
img = np.array(img) / 255.0
img = img.reshape(1, IMG_SIZE, IMG_SIZE, 1)
return img
if uploaded_file:
try:
image = preprocess_dicom_bytes(uploaded_file.read())
prob = model.predict(image)[0][0]
label = "Pneumonia" if prob >= 0.45 else "No Pneumonia"
st.success("Prediction complete")
st.write(f"**Predicted Class:** {label}")
st.write(f"**Probability:** {prob:.2f}")
except Exception as e:
st.error(f"Error processing file: {e}")
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