Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,51 @@
-import altair as alt
-import numpy as np
-import pandas as pd
 import streamlit as st
-
-"""
-# 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])),
-    ))
+import tensorflow as tf
+from PIL import Image
+import numpy as np
+import os
+
+# Load the saved model
+@st.cache_resource
+def load_model():
+    model = tf.keras.models.load_model(r'C:\Users\atulb\OneDrive\Documents\Desktop\Model\pneumonia_cnn_model.keras')  # Use relative path for deployment
+    return model
+
+model = load_model()
+
+st.title("🫁 Pneumonia Detection from Chest X-ray Images")
+
+st.markdown("Upload your own X-ray or try one of the sample images below.")
+
+# === Sample Image Section ===
+sample_images = {
+    "Choose a sample image": None,
+    "🧍 Normal Sample": "samples/normal.jpg",
+    "🤒 Pneumonia Sample": "samples/pneumonia.jpg"
+}
+
+selected_sample = st.selectbox("📂 Select a sample image", list(sample_images.keys()))
+uploaded_file = st.file_uploader("📁 Or upload a chest X-ray image...", type=["jpg", "jpeg", "png"])
+
+# Determine which image to use
+if selected_sample != "Choose a sample image":
+    image_path = sample_images[selected_sample]
+    image = Image.open(image_path).convert("RGB")
+    st.image(image, caption=f'🖼️ {selected_sample}', use_column_width=True)
+elif uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption='🖼️ Uploaded Image', use_column_width=True)
+else:
+    image = None
+
+# === Predict Button ===
+if image and st.button('🔍 Predict'):
+    img = image.resize((150, 150))
+    img_array = np.array(img) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)
+
+    if prediction[0][0] > 0.5:
+        st.error("🩺 **Prediction: Pneumonia Detected**")
+    else:
+        st.success("✅ **Prediction: Normal**")