app.py

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)