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import streamlit as st
import tensorflow as tf
import pickle
from tensorflow.keras.preprocessing import image
import numpy as np
# Function to preprocess the image
def preprocess_image(img_path, img_height, img_width, model_type="CNN"):
# Load the image and convert to grayscale
img = image.load_img(img_path, target_size=(img_height, img_width), color_mode='grayscale')
img_array = image.img_to_array(img)
# Normalize the image array
img_array = img_array / 255.0 # Normalize for all models
if model_type in ["Logistic Regression", "Decision Tree"]:
img_array = img_array.flatten() # Flatten for Logistic Regression and Decision Tree
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
else:
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension for CNN and ANN
return img_array
# Load the Keras model
@st.cache_resource
def load_keras_model():
return tf.keras.models.load_model('best_model_.keras')
# Load the other models
@st.cache_resource
def load_pickle_model(model_path):
with open(model_path, 'rb') as f:
return pickle.load(f)
# Define model paths and validation accuracies
models_info = {
"ANN Model": {
"path": "ann_model.pkl",
"accuracy": 0.60
},
"Decision Tree": {
"path": "decision_tree_classifier_model.pkl",
"accuracy": 0.70
},
"Logistic Regression": {
"path": "logistic_regression_model.pkl",
"accuracy": 0.60
},
"CNN Model": {
"path": "best_model_.keras",
"accuracy": 0.90
}
}
# Streamlit UI
st.title("X-ray Image Classification")
st.write("Upload an X-ray image to classify it as Normal or Pneumonia.")
# Model selection
model_name = st.selectbox("Choose a model:", list(models_info.keys()))
# Display selected model accuracy
st.write(f"Selected Model: {model_name}")
st.write(f"Validation Accuracy: {models_info[model_name]['accuracy'] * 100:.2f}%")
# Load the selected model
if model_name == "CNN Model":
model = load_keras_model()
else:
model = load_pickle_model(models_info[model_name]["path"])
# File uploader for image
uploaded_file = st.file_uploader("Choose an X-ray image...", type="jpeg")
if uploaded_file is not None:
with open("temp.jpeg", "wb") as f:
f.write(uploaded_file.getbuffer())
# Use the appropriate preprocessing for the selected model
img_height, img_width = 224, 224 # Use the same dimensions as used during training
preprocessed_img = preprocess_image(
"temp.jpeg",
img_height,
img_width,
model_type=model_name # Pass the model name directly
)
st.image(uploaded_file, caption="Uploaded X-ray Image", use_column_width=True)
# Prediction logic
prediction = model.predict(preprocessed_img)
prediction_label = "Pneumonia" if prediction[0] > 0.5 else "Normal"
st.write(f"Prediction: {prediction_label} (Model: {model_name})")
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