Update src/streamlit_app.py

src/streamlit_app.py

@@ -1,40 +1,37 @@
-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 numpy as np
+from tensorflow.keras.models import load_model # type: ignore
+from PIL import Image
+
+def preprocess_image(img):
+    img = img.convert('L')  # Convert to grayscale
+    img = img.resize((64, 64))  # Resize to match model input
+    img = np.array(img) / 255.0  # Normalize to [0, 1]
+    img = np.expand_dims(img, axis=-1)  # Add channel dimension: (64, 64, 1)
+    img = np.expand_dims(img, axis=0)   # Add batch dimension: (1, 64, 64, 1)
+    return img
+
+model = load_model("model/Pneumonia_Detector.keras")
+
+st.title("Pneumonia Detector Using CNN")
+uploaded_file = st.file_uploader("Upload an Image for Prediction", type=['jpg', 'png', 'jpeg'])
+col1, col2 = st.columns(2)
+
+if uploaded_file is not None:
+    image_pil = Image.open(uploaded_file)
+    thumbnail = image_pil.copy()
+    thumbnail.thumbnail((200, 200)) 
+    col1.image(thumbnail, caption="Preview", width=100)
+
+    if col1.button("Predict"):
+        img_array = preprocess_image(image_pil)
+        prediction = model.predict(None,img_array)
+        predicted_class = "Pneumonia" if prediction[0][0] > 0.5 else "Normal"
+        confidence = prediction[0][0] if prediction[0][0] > 0.5 else 1 - prediction[0][0]
+
+        col2.write("### Predicted Class:")
+        if predicted_class == "Pneumonia":
+            col2.warning("Pneumonia")
+        else:
+            col2.success("Normal")
+        col2.write(f"### Confidence Level: {confidence:.2%}")