Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,144 @@
-import altair as alt
+import io
 import numpy as np
-import pandas as pd
 import streamlit as st
+import tensorflow as tf
+from tensorflow import keras
+import pydicom
 
-"""
-# Welcome to Streamlit!
-
-Edit `/streamlit_app.py` to customize this app to your heart's desire :heart:.
-If you have any questions, checkout our [documentation](https://docs.streamlit.io) and [community
-forums](https://discuss.streamlit.io).
-
-In the meantime, below is an example of what you can do with just a few lines of code:
-"""
-
-num_points = st.slider("Number of points in spiral", 1, 10000, 1100)
-num_turns = st.slider("Number of turns in spiral", 1, 300, 31)
-
-indices = np.linspace(0, 1, num_points)
-theta = 2 * np.pi * num_turns * indices
-radius = indices
-
-x = radius * np.cos(theta)
-y = radius * np.sin(theta)
-
-df = pd.DataFrame({
-    "x": x,
-    "y": y,
-    "idx": indices,
-    "rand": np.random.randn(num_points),
-})
-
-st.altair_chart(alt.Chart(df, height=700, width=700)
-    .mark_point(filled=True)
-    .encode(
-        x=alt.X("x", axis=None),
-        y=alt.Y("y", axis=None),
-        color=alt.Color("idx", legend=None, scale=alt.Scale()),
-        size=alt.Size("rand", legend=None, scale=alt.Scale(range=[1, 150])),
-    ))
+# ----------------------------------------------------
+# App Configuration
+# ----------------------------------------------------
+st.set_page_config(
+    page_title="Pneumonia Detection (Chest X-ray) – Clinical Decision Support",
+    layout="centered"
+)
+
+st.title("Pneumonia Detection (Chest X-ray) – Clinical Decision Support")
+st.caption(
+    "Upload one or more Chest X-ray DICOM files (.dcm). "
+    "Adjust the decision threshold and submit to obtain a probability-based binary prediction. "
+    "This system is intended for clinical decision support and does not replace professional medical judgment."
+)
+
+# ----------------------------------------------------
+# Load Model
+# ----------------------------------------------------
+MODEL_PATH = "model.keras"
+
+@st.cache_resource
+def load_model():
+    try:
+        return keras.models.load_model(MODEL_PATH)
+    except Exception:
+        keras.config.enable_unsafe_deserialization()
+        return keras.models.load_model(MODEL_PATH, safe_mode=False)
+
+model = load_model()
+
+input_shape = model.input_shape
+img_size = int(input_shape[1]) if input_shape and input_shape[1] else 256
+expected_channels = int(input_shape[-1]) if input_shape and input_shape[-1] else 3
+
+# ----------------------------------------------------
+# Threshold Slider (DEFAULT = 0.37 for ResNet)
+# ----------------------------------------------------
+st.subheader("Model Parameters")
+
+threshold = st.slider(
+    "Decision Threshold",
+    min_value=0.01,
+    max_value=0.99,
+    value=0.37,  # <-- DEFAULT CHANGED HERE
+    step=0.01,
+    help="If predicted probability ≥ threshold → Pneumonia. Otherwise → Not Pneumonia."
+)
+
+# ----------------------------------------------------
+# File Upload
+# ----------------------------------------------------
+st.subheader("Upload Chest X-ray DICOM Files")
+
+uploaded_files = st.file_uploader(
+    "Select one or multiple DICOM files (.dcm)",
+    type=["dcm"],
+    accept_multiple_files=True
+)
+
+col1, col2 = st.columns(2)
+with col1:
+    submit = st.button("Submit", type="primary", use_container_width=True)
+with col2:
+    clear = st.button("Clear", use_container_width=True)
+
+if clear:
+    st.experimental_rerun()
+
+# ----------------------------------------------------
+# Helper Functions
+# ----------------------------------------------------
+def read_dicom(file):
+    data = file.read()
+    dcm = pydicom.dcmread(io.BytesIO(data))
+    img = dcm.pixel_array.astype(np.float32)
+
+    img = (img - img.min()) / (img.max() - img.min() + 1e-8)
+    return img
+
+def preprocess(img):
+    x = tf.convert_to_tensor(img[..., None], dtype=tf.float32)
+    x = tf.image.resize(x, (img_size, img_size))
+    x = tf.clip_by_value(x, 0.0, 1.0)
+    x = x.numpy()
+
+    # If model expects 3 channels (ResNet)
+    if expected_channels == 3 and x.shape[-1] == 1:
+        x = np.repeat(x, 3, axis=-1)
+
+    x = np.expand_dims(x, axis=0)
+    return x.astype(np.float32)
+
+def get_probability(x):
+    prediction = model.predict(x, verbose=0)
+
+    if isinstance(prediction, (list, tuple)):
+        prob = float(np.ravel(prediction[-1])[0])
+    else:
+        prob = float(np.ravel(prediction)[0])
+
+    return max(0.0, min(1.0, prob))
+
+# ----------------------------------------------------
+# Inference Section
+# ----------------------------------------------------
+st.subheader("Prediction Results")
+
+if submit:
+    if not uploaded_files:
+        st.warning("Please upload at least one DICOM file before clicking Submit.")
+    else:
+        with st.spinner("Processing uploaded file(s)..."):
+            for file in uploaded_files:
+                try:
+                    image_array = read_dicom(file)
+                    x_input = preprocess(image_array)
+                    probability = get_probability(x_input)
+
+                    predicted_label = "Pneumonia" if probability >= threshold else "Not Pneumonia"
+
+                    st.write(
+                        f"For the uploaded file '{file.name}', the model estimates a pneumonia probability of "
+                        f"{probability * 100:.2f}%. Based on the selected decision threshold of {threshold:.2f}, "
+                        f"the predicted outcome is '{predicted_label}'."
+                    )
+
+                except Exception as e:
+                    st.error(
+                        f"For the uploaded file '{file.name}', the system could not generate a prediction. "
+                        f"Reason: {str(e)}."
+                    )
+
+st.divider()
+st.caption(
+    "Clinical Notice: This application is designed for decision support purposes only. "
+    "Final diagnosis and treatment decisions must be made by qualified healthcare professionals."
+)