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import streamlit as st
import tensorflow as tf
import numpy as np
from PIL import Image
import pandas as pd
import matplotlib.pyplot as plt
# Load the trained model
model_path = "flower-model.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)
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("Bluemen erkenner")
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='Uploaded Image.', use_column_width=True)
st.write("")
st.write("Identifiezieren...")
predictions = predict_flower(image)
# Display predictions as a DataFrame
st.write("### Prediction Probabilities")
df = pd.DataFrame(predictions.items(), columns=["Flower", "Probability"])
st.dataframe(df)
# Example images
st.sidebar.title("Examples")
example_images = ["Blume/rose.png", "Blume/sunflower.png", "Blume/dandelion.png"]
for example_image in example_images:
st.sidebar.image(example_image, use_column_width=True)
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