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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
# Load the trained model
model_path = "flower-model-ResNet50.keras"
model = tf.keras.models.load_model(model_path)
# Define the core prediction function
def predict_flower(image):
# Preprocess image
image = image.resize((150, 150)) # Resize the image to 150x150
image = image.convert('RGB') # Ensure image has 3 channels
image = np.array(image) / 255.0 # Normalize the image to [0, 1]
image = np.expand_dims(image, axis=0) # Add batch dimension
# Predict
prediction = model.predict(image)
# Apply softmax to get probabilities for each class
probabilities = tf.nn.softmax(prediction, axis=1)
# Map probabilities to Flower classes
class_names = ['daisy', 'dandelion', 'rose', 'sunflower', 'tulip']
probabilities_dict = {flower_class: round(float(probability), 2) for flower_class, probability in zip(class_names, probabilities.numpy()[0])}
return probabilities_dict
# Streamlit interface
st.title("Der Bluemenerkenner")
st.write("Welche Blume wächst in ihrem Garten?")
# Upload image
uploaded_image = st.file_uploader("Lade dein Bild hoch...", type=["jpg", "png"])
if uploaded_image is not None:
image = Image.open(uploaded_image)
st.image(image, caption='Hochgeladenes Bild.', use_column_width=True)
st.write("")
st.write("Identifizieren...")
predictions = predict_flower(image)
# Log the predictions
st.write("Vorhersagen für jede Klasse:")
for flower_class, probability in predictions.items():
st.write(f"{flower_class}: {probability}")
# Find the flower with the highest probability
highest_probability_flower = max(predictions, key=predictions.get)
# Display the flower with the highest probability
st.write(f"Es ist eine: **{highest_probability_flower}**")
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