app.py

import streamlit as st

# ---------------------------------------------------
# ✅ MUST BE FIRST STREAMLIT COMMAND
# ---------------------------------------------------
st.set_page_config(
    page_title="Image Classifier",
    layout="centered"
)

import joblib
import numpy as np
import cv2
import os

# ---------------------------------------------------
# Feature Extractor (SAME AS TRAINING)
# ---------------------------------------------------
def extract_rgb_hist(image_path):
    img = cv2.imread(image_path)
    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

    hist_r = cv2.calcHist([img], [0], None, [256], [0, 256]).flatten()
    hist_g = cv2.calcHist([img], [1], None, [256], [0, 256]).flatten()
    hist_b = cv2.calcHist([img], [2], None, [256], [0, 256]).flatten()

    return np.concatenate((hist_r, hist_g, hist_b))


# ---------------------------------------------------
# Load Models (ONCE)
# ---------------------------------------------------
@st.cache_resource
def load_models():
    svm = joblib.load("svm_model_1.pkl")      # Pipeline model
    dt  = joblib.load("decision_tree_model.pkl")
    rf  = joblib.load("random_forest_model.pkl")
    return svm, dt, rf


# ✅ CALL AFTER set_page_config
svm_model, dt_model, rf_model = load_models()


# ---------------------------------------------------
# Streamlit UI
# ---------------------------------------------------
st.title("🚗 Image Classification App by Delumi!")
st.write("Upload an image and select a model to classify it.")

model_choice = st.selectbox(
    "Choose a Model:",
    ("SVM (Best Tuned)", "Decision Tree", "Random Forest")
)

uploaded_file = st.file_uploader(
    "Upload Image",
    type=["jpg", "jpeg", "png"]
)

if uploaded_file is not None:
    temp_path = "temp_image.jpg"

    with open(temp_path, "wb") as f:
        f.write(uploaded_file.read())

    st.image(temp_path, caption="Uploaded Image", use_column_width=True)

    features = extract_rgb_hist(temp_path).reshape(1, -1)

    if model_choice == "SVM (Best Tuned)":
        model = svm_model
    elif model_choice == "Decision Tree":
        model = dt_model
    else:
        model = rf_model

    prediction = model.predict(features)[0]

    st.subheader("🔮 Prediction Result")
    st.write(f"**Model Used:** {model_choice}")
    st.success(f"**Predicted Class:** {prediction}")

    os.remove(temp_path)