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import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np
# Load the model
model = load_model('cnn_model.h5')
# Function to process the uploaded image
def process_image(img):
img = img.convert("RGB") # Convert to RGB
img = img.resize((64, 64)) # Resize to match model input
img = np.array(img) / 255.0 # Normalize pixel values
img = np.expand_dims(img, axis=0) # Expand dimensions for batch processing
return img
# Streamlit App
st.set_page_config(page_title="Pneumonia Detector", page_icon="🩺", layout="centered")
# Apply custom styling
st.markdown(
"""
<style>
.main {background-color: #e3f2fd;} /* Light blue background */
h1 {color: #008080; text-align: center;} /* Teal color title */
.stButton button {background-color: #008080; color: white; font-size: 18px;} /* Stylish button */
.result-box {padding: 10px; border-radius: 10px; font-size: 20px; text-align: center; font-weight: bold;}
</style>
""",
unsafe_allow_html=True,
)
# Title
st.title("🩺 Pneumonia Detection System")
st.write("Upload a chest X-ray image to check if it indicates Pneumonia or is Normal.")
# File uploader
file = st.file_uploader("📷 Upload an Image", type=["jpg", "jpeg", "png"])
if file is not None:
# Display uploaded image
img = Image.open(file)
st.image(img, caption="Uploaded Image")
# Predict button
if st.button("🔍 Predict"):
with st.spinner("Analyzing... ⏳"):
image = process_image(img)
predictions = model.predict(image)
predicted_class = np.argmax(predictions)
# Class names
class_names = ["NORMAL", "PNEUMONIA"]
result_text = class_names[predicted_class]
# Dynamic coloring for results
color = "green" if predicted_class == 0 else "red"
# Display result
st.markdown(
f'<div class="result-box" style="background-color: {color}; color: white;">{result_text}</div>',
unsafe_allow_html=True,
)
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