First commit

.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
+*.keras filter=lfs diff=lfs merge=lfs -text

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+from tensorflow.keras.applications.imagenet_utils import preprocess_input
+import os
+
+# Load your frozen model
+model = tf.keras.models.load_model("final_trashnet_transfer_learning_model.keras")
+
+# Mapping of original classes to broader categories
+class_mapping = {
+    0: "Compostable",  # compostable
+    1: "Recyclables",  # recyclable
+    2: "Trash",  #trash
+}
+# Define a function to preprocess the input image
+def preprocess_image(image):
+    # Resize the image to 128*128 (as required by your model)
+    image = image.resize((128, 128))
+    # Convert the image to a NumPy array and normalize it
+    img_array = img_to_array(image)
+    img_array = preprocess_input(img_array)
+    # Ensure the image has the correct shape (32, 32, 3)
+    img_array = np.expand_dims(img_array, axis=0) 
+    return img_array
+
+
+# Define the prediction function
+def classify_trash(image):
+    processed_image = preprocess_image(image)
+    predictions = model.predict(processed_image)
+    print(predictions)
+    class_index = np.argmax(predictions)
+    confidence = np.max(predictions)
+    predicted_class = class_mapping[class_index]
+    return f"Predicted Category: {predicted_class}", f"Confidence: {confidence*100:.2f}"
+
+# Function to gather example images dynamically
+def get_example_images():
+    example_images = []
+    base_dir = "examples"
+    categories = ["Compostable", "Recyclables", "Trash"]
+    for category in categories:
+        folder_path = os.path.join(base_dir, category)
+        if os.path.exists(folder_path):
+            example_images += [
+                os.path.join(folder_path, img) for img in os.listdir(folder_path) if img.endswith((".jpg", ".png"))
+            ]
+    return example_images
+
+# Example images from all categories
+example_images = get_example_images()
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=classify_trash,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=[gr.Textbox(label="Predicted Category"), gr.Textbox(label="Confidence")],
+    examples= example_images,
+    title="Trash Classifier",
+    description="Upload an image of trash, and the model will classify it into 'Compostable', 'Recyclables' and 'Trash' based on its category."
+)
+
+# Run the app
+if __name__ == "__main__":
+    interface.launch()

final_trashnet_transfer_learning_model.keras

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8fb9b9aa6c4354259fcdfa66ab05e3d3a162be7f3b6306aa487e6f14ad8510fe
+size 98138568

requirements.txt

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+gradio==3.43.0
+tensorflow==2.13.0
+numpy==1.24.3
+Pillow==9.5.0