Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,87 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
+import numpy as np
+import tensorflow as tf
+import pydicom
+import cv2
+
+# --------------------------------------------------
+# App Config
+# --------------------------------------------------
+st.set_page_config(page_title="Pneumonia Detection", layout="centered")
+
+st.title("🫁 Pneumonia Detection from Chest X-ray")
+st.write("Upload a chest X-ray (DICOM) to detect pneumonia.")
+
+# --------------------------------------------------
+# Load Serialized Model (Inference Only)
+# --------------------------------------------------
+@st.cache_resource
+def load_model():
+    return tf.keras.models.load_model(
+        "pneumonia_final_model.keras",
+        compile=False   # avoids optimizer warnings
+    )
+
+model = load_model()
+
+# --------------------------------------------------
+# Preprocess DICOM
+# --------------------------------------------------
+def preprocess_dicom(uploaded_file):
+    dicom = pydicom.dcmread(uploaded_file)
+    img = dicom.pixel_array.astype(np.float32)
+
+    img = (img - img.min()) / (img.max() - img.min() + 1e-6)
+    img = cv2.resize(img, (224, 224))
+
+    # grayscale → RGB
+    img = np.stack([img] * 3, axis=-1)
+    img = np.expand_dims(img, axis=0)
+
+    return img
+
+# --------------------------------------------------
+# File Upload
+# --------------------------------------------------
+uploaded_file = st.file_uploader(
+    "Upload Chest X-ray (.dcm file)",
+    type=["dcm"]
+)
+
+# --------------------------------------------------
+# Prediction
+# --------------------------------------------------
+if uploaded_file is not None:
+    try:
+        image = preprocess_dicom(uploaded_file)
+
+        with st.spinner("Analyzing X-ray..."):
+            probs = model.predict(image)[0]
+
+        # ---- 3-class → binary mapping ----
+        pneumonia_prob = float(probs[2])
+        non_pneumonia_prob = float(probs[0] + probs[1])
+
+        if pneumonia_prob >= 0.5:
+            prediction = "Pneumonia"
+            confidence = pneumonia_prob
+            st.error(f"⚠️ {prediction} detected")
+        else:
+            prediction = "Non-Pneumonia"
+            confidence = non_pneumonia_prob
+            st.success(f"✅ {prediction}")
+
+        st.subheader("Prediction Result")
+        st.write(f"**Prediction:** {prediction}")
+        st.write(f"**Confidence:** {confidence:.2%}")
+
+        # Optional: show raw probabilities (for transparency)
+        with st.expander("View raw class probabilities"):
+            st.write({
+                "Normal": float(probs[0]),
+                "No Lung Opacity": float(probs[1]),
+                "Lung Opacity (Pneumonia)": float(probs[2])
+            })
 
-"""
-# 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])),
-    ))
+    except Exception as e:
+        st.error(f"Error processing file: {e}")