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db62b1a 8c3a5b9 d81c582 8c3a5b9 d81c582 8c3a5b9 | 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 | import streamlit as st
import numpy as np
from tensorflow.keras.models import load_model # type: ignore
from PIL import Image
import os
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/PneumoniaDetectionModel.keras")
current_dir = os.path.dirname(os.path.abspath(__file__))
model_path = os.path.join(current_dir, "../model/PneumoniaDetectionModel.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%}") |