src/streamlit_app.py

import streamlit as st
from tensorflow.keras.models import load_model
from PIL import Image
import numpy as np

model = load_model('src/flower.h5')

def process_image(img):
    img = img.resize((64, 64))
    img = np.array(img)
    img = img / 255.0
    img = np.expand_dims(img, axis=0)
    return img

st.title("Flower Image Recognition")
st.write("Upload a flower image and the model will predict the type.")

file = st.file_uploader('Select an image', type=['jpg', 'jpeg', 'png'])

if file is not None:
    img = Image.open(file)
    st.image(img, caption='Uploaded Image')
    
    image = process_image(img)
    prediction = model.predict(image)
    predicted_class = np.argmax(prediction)
    
    class_names = ['Dandelion', 'Daisy', 'Sunflower', 'Tulip', 'Rose']
    
    st.write(f"Prediction: {class_names[predicted_class]}")