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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+recycling-model_transferlearning.keras filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,74 @@
+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+model_path = "recycling-model_transferlearning.keras"
+model = tf.keras.models.load_model(model_path)
+# Define the core prediction function
+def predict_recycling(image):
+    # Preprocess image
+    print(type(image))
+    image = Image.fromarray(image.astype('uint8'))  # Convert numpy array to PIL image
+    image = image.resize((150, 150))  # Resize the image to 150x150
+    image = np.array(image)
+    image = np.expand_dims(image, axis=0)  # Expand dimensions to create batch size of 1
+
+    # Predict
+    prediction = model.predict(image)
+    
+    # Assuming the model's output layer uses softmax activation and there are three outputs
+    prediction = prediction.flatten()
+    predictions = np.round(prediction, 2)  # Flatten the predictions and round them
+
+    # Separate the probabilities for each class
+    p_battery = predictions[0]  # Probability for battery
+    p_biological = predictions[1]   # Probability for biological
+    p_brownglass = predictions[2]  # Probability for brown-glass
+    p_cardboard = predictions[3]  # Probability for cardboard
+    p_clothes = predictions[4]  # Probability for clothes
+    p_greenglass = predictions[5]  # Probability for green-glass
+    p_metal = predictions[6]  # Probability for metal
+    p_paper = predictions[7]  # Probability for paper
+    p_plastic = predictions[8]  # Probability for plastic
+    p_shoes = predictions[9]  # Probability for shoes
+    p_whiteglass = predictions[10]  # Probability for white-glass
+
+    return {
+        'battery': p_battery,
+        'biological': p_biological,
+        'brown-glass': p_brownglass,
+        'cardboard': p_cardboard,
+        'clothes': p_clothes,
+        'green-glass': p_greenglass,
+        'metal': p_metal,
+        'paper': p_paper,
+        'plastic': p_plastic,
+        'shoes': p_shoes,
+        'white-glass': p_whiteglass
+    }
+# Create the Gradio interface
+input_image = gr.Image()
+interface = gr.Interface(
+    fn=predict_recycling,
+    inputs=input_image,
+    outputs=gr.Label(),
+    examples=["test/battery1.jpg", "test/battery2.jpg", "test/battery3.jpg",
+              "test/biological1.jpg", "test/biological2.jpg", "test/biological3.jpg",
+              "test/brown-glass1.jpg", "test/brown-glass2.jpg", "test/brown-glass3.jpg",
+              "test/cardboard1.jpg", "test/cardboard2.jpg", "test/cardboard3.jpg",
+              "test/clothes1.jpg", "test/clothes2.jpg", "test/clothes3.jpg",
+              "test/green-glass1.jpg", "test/green-glass2.jpg", "test/green-glass3.jpg",
+              "test/metal1.jpg", "test/metal2.jpg", "test/metal3.jpg",
+              "test/paper1.jpg", "test/paper2.jpg", "test/paper3.jpg",
+              "test/plastic1.jpg", "test/plastic2.jpg", "test/plastic3.jpg",
+              "test/shoes1.jpg", "test/shoes2.jpg", "test/shoes3.jpg",
+              "test/white-glass1.jpg", "test/white-glass2.jpg", "test/white-glass3.jpg"],
+    title="Bildklassifikation für Recycling-Materialien",
+    description="Dieses Tool klassifiziert Bilder in verschiedene Recycling-Kategorien. Bitte lade ein Bild hoch, benutze die Kamera oder verwende ein Beispiel von unten, um die Klassifikation zu sehen.",
+    theme = gr.themes.Soft(
+    primary_hue="emerald",
+    secondary_hue="emerald",
+    ).set(
+        background_fill_primary='*neutral_100'
+    ))
+interface.launch()

recycling-model_transferlearning.keras

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1e4efe3562b9b1c36eccc8b31e0ca5ecfcbe2e149687d1e733a4f5da622fb83c
+size 250756852