Create app.py

app.py

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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 = train.class_names  # 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}**")
+