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

MODEL_PATH = "src/flower_model.keras"
IMAGE_SIZE = 224

# DOĞRU 104 class sırası
class_names = sorted([
    "pink primrose","hard-leaved pocket orchid","canterbury bells",
    "sweet pea","english marigold","tiger lily","moon orchid",
    "bird of paradise","monkshood","globe thistle",
    "snapdragon","colt's foot","king protea","spear thistle",
    "yellow iris","globe-flower","purple coneflower",
    "peruvian lily","balloon flower","giant white arum lily",
    "fire lily","pincushion flower","fritillary","red ginger",
    "grape hyacinth","corn poppy","prince of wales feathers",
    "stemless gentian","artichoke","sweet william",
    "carnation","garden phlox","love in the mist","mexican aster",
    "alpine sea holly","ruby-lipped cattleya","cape flower",
    "great masterwort","siam tulip","lenten rose",
    "barbeton daisy","daffodil","sword lily","poinsettia",
    "bolero deep blue","wallflower","marigold","buttercup",
    "oxeye daisy","common dandelion","petunia","wild pansy",
    "primula","sunflower","pelargonium","bishop of llandaff",
    "gaura","geranium","orange dahlia","pink-yellow dahlia",
    "cautleya spicata","japanese anemone","black-eyed susan",
    "silverbush","californian poppy","osteospermum",
    "spring crocus","iris","windflower","tree poppy",
    "gazania","azalea","water lily","rose",
    "thorn apple","morning glory","passion flower","lotus",
    "toad lily","anthurium","frangipani","clematis",
    "hibiscus","columbine","desert-rose","tree mallow",
    "magnolia","cyclamen","watercress","canna lily",
    "hippeastrum","bee balm","ball moss","foxglove",
    "bougainvillea","camellia","mallow","mexican petunia",
    "bromelia","blanket flower","trumpet creeper","blackberry lily"
])

@st.cache_resource
def load_model():
    return tf.keras.models.load_model(MODEL_PATH)

model = load_model()

st.title("🌸 Flower Classifier")

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

if uploaded_file:
    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, use_container_width=True)

    image = image.resize((IMAGE_SIZE, IMAGE_SIZE))
    img_array = np.array(image) / 255.0   # Eğitimde bunu kullanmıştık
    img_array = np.expand_dims(img_array, axis=0)

    prediction = model.predict(img_array)
    predicted_index = int(np.argmax(prediction))
    confidence = float(np.max(prediction))

    st.write("Predicted index:", predicted_index)

    flower_name = class_names[predicted_index]

    st.success(f"🌼 Prediction: {flower_name}")
    st.info(f"Confidence: {confidence:.2%}")