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import streamlit as st
import numpy as np
import cv2
import joblib
import tensorflow as tf
from tensorflow.keras.applications import (
ResNet50, VGG16, EfficientNetV2B0, InceptionV3, ResNet101, DenseNet201
)
from tensorflow.keras.preprocessing import image
import os
# Define available models
MODELS = {
'ResNet50': ResNet50(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling="avg"),
'VGG16': VGG16(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling="avg"),
'EfficientNetV2B0': EfficientNetV2B0(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling="avg"),
'InceptionV3': InceptionV3(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling="avg"),
'ResNet101': ResNet101(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling="avg"),
'DenseNet201': DenseNet201(weights='imagenet', include_top=False, input_shape=(224, 224, 3), pooling="avg")
}
# Load trained models
MODEL_PATHS = {model_name: f"{model_name}_catboost.pkl" for model_name in MODELS}
trained_models = {}
# Load the trained models into memory
for model_name, path in MODEL_PATHS.items():
if os.path.exists(path):
trained_models[model_name] = joblib.load(path)
# Define class names (modify based on dataset)
CLASS_NAMES = ['ADI','DEB','LYM','MUC','MUS','NOR','STR','TUM'] # Update with actual labels
# Streamlit UI
st.title("Multi-Model Image Classifier")
st.markdown("Upload an image and select which models and classes to use for prediction.")
# Upload an image
uploaded_file = st.file_uploader("Upload an image...", type=["jpg", "png", "jpeg"])
# Select Models
selected_models = st.multiselect("Select models for prediction:", list(trained_models.keys()))
# Select Classes
selected_classes = st.multiselect("Select classes to predict:", CLASS_NAMES, default=CLASS_NAMES)
# Function to preprocess image
def preprocess_image(img_path):
img = image.load_img(img_path, target_size=(224, 224))
img_array = image.img_to_array(img) / 255.0 # Normalize
img_array = np.expand_dims(img_array, axis=0)
return img_array
# Perform prediction
if uploaded_file and selected_models:
# Read and preprocess the image
image_np = np.array(bytearray(uploaded_file.read()), dtype=np.uint8)
image_np = cv2.imdecode(image_np, cv2.IMREAD_COLOR)
image_np = cv2.resize(image_np, (224, 224)) / 255.0
image_np = np.expand_dims(image_np, axis=0)
# Extract Features
extracted_features = {}
for model_name in selected_models:
extracted_features[model_name] = MODELS[model_name].predict(image_np)
# Predict using each selected CatBoost model
predictions = {}
for model_name in selected_models:
X_input = extracted_features[model_name]
catboost_model = trained_models[model_name]
y_pred = catboost_model.predict_proba(X_input)[0] # Get probabilities
predictions[model_name] = y_pred
# Display results
st.subheader("Prediction Results")
for model_name, y_pred in predictions.items():
st.write(f"**Model: {model_name}**")
for i, class_name in enumerate(CLASS_NAMES):
if class_name in selected_classes:
st.write(f" - {class_name}: **{y_pred[i]:.4f}**")
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