import os
os.system("pip install tensorflow")
os.system("pip install scikit-learn")

import streamlit as st
import tensorflow as tf
import numpy as np
import pickle
from PIL import Image

# Constants
MODEL_PATH = "image_classification.h5"
LABEL_ENCODER_PATH = "le.pkl"
EXPECTED_SIZE = (64, 64)  # Update this based on your model's input shape

def load_resources():
    """Load model and label encoder."""
    model = tf.keras.models.load_model(MODEL_PATH)
    with open(LABEL_ENCODER_PATH, "rb") as f:
        label_encoder = pickle.load(f)
    return model, label_encoder

# Load resources
model, label_encoder = load_resources()

def preprocess_image(image):
    """Resize image to match model input shape."""
    image = image.resize(EXPECTED_SIZE)  # Resize to match model input
    image_array = np.array(image)  # Convert to numpy array
    image_array = np.expand_dims(image_array, axis=0)  # Add batch dimension
    return image_array

def predict(image):
    """Predict the class of the uploaded image."""
    image_array = preprocess_image(image)
    preds = model.predict(image_array)
    class_index = np.argmax(preds)
    return label_encoder.inverse_transform([class_index])[0]

# Streamlit UI
st.set_page_config(page_title="Image Classifier", layout="wide")
st.markdown("""
    <h1 style='text-align: center; color: #4A90E2;'>🖼️ Image Classification App</h1>
    <p style='text-align: center; font-size: 18px;'>Upload an image and let our model classify it for you!</p>
    <hr>
""", unsafe_allow_html=True)

st.sidebar.header("Upload Your Image")
uploaded_file = st.sidebar.file_uploader("Choose an image", type=["jpg", "png", "jpeg"], help="Supported formats: JPG, PNG, JPEG")

if uploaded_file:
    image = Image.open(uploaded_file)
    st.image(image, caption="📸 Uploaded Image", use_column_width=True)
    
    if st.button("🔍 Classify Image", use_container_width=True):
        prediction = predict(image)
        st.success(f"🎯 Predicted Class: {prediction}")