Update app.py

app.py

@@ -1,57 +1,50 @@
-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)
+import streamlit as st
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# 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("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)
+    
+    # Find the flower with the highest probability
+    highest_probability_flower = max(predictions, key=predictions.get)
+    highest_probability_value = predictions[highest_probability_flower]
+    
+    # Display the flower with the highest probability
+    st.write(f"Es ist eine: **{highest_probability_flower}** mit einer Wahrscheinlichkeit von **{highest_probability_value * 100}%**.")