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Bins.json

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+ {"output_class": ["Plastic bottle/Can to deposit in Supermarkets", "Big Cardboard bin", "Unrecyclable garbage", "Glass - Purple bin", "Organic waste - Composter", "Grocery Packages - Orange bin", "Paper - Blue bin"], "bin_images": ["https://ynet-pic1.yit.co.il/cdn-cgi/image/format=auto/picserver5/crop_images/2021/11/30/S1WrTikNKK/S1WrTikNKK_0_360_768_432_0_x-large.jpg", "https://www.greenkoala.co.il/wp-content/uploads/2017/08/WhatsApp-Image-2021-09-29-at-13.12.20.jpeg", "https://mediline.org.il/sites/mediline/cache/w_500,h_500,mode_pad/ofktpql3.jpg", "https://www.tmir.org.il/Download/Gallery/660-260-186_Pah_Sagol_pics4_14112021115647.jpg", "https://static.aujardin.info/cache/th/img9/composter-600x450.jpg", "https://cdn.speedsize.com/bce19381-5898-42c8-b41a-5fe7eb41262f/https://d3m9l0v76dty0.cloudfront.net/system/photos/1772748/large/5613563632c45ebfb2b39031b8090a51.jpg", "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRn8eiuf5_6L6Lh5lDaFxwezeyZSVpk2PDOjYrH0cXDHDqihG5aBt3V7AlfvYSST3mdLvw&usqp=CAU"]}

README.md

@@ -1,13 +1,14 @@
 ---
-title: Smart Waste
-emoji: 👁
-colorFrom: gray
-colorTo: gray
+title: SmartWaste
+emoji: 🐢
+colorFrom: purple
+colorTo: red
 sdk: gradio
-sdk_version: 4.14.0
+sdk_version: 4.10.0
 app_file: app.py
 pinned: false
 license: apache-2.0
 ---
+For more info check the git repo at https://github.com/ArielDrabkin/SmartWaste
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+from predict import image_identity
+import gradio as gr
+import os
+
+with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as iface:
+ logo_url = os.path.join(os.path.dirname(__file__), "images/logo.png")
+ # Displaying the logo
+ with gr.Row(elem_classes="center-image"):
+ gr.Image(logo_url, show_label=False, show_download_button=False,
+ show_share_button=False)
+ iface_main = gr.Interface(image_identity,
+ inputs = gr.Image(label="Upload your image"), # Adding a label to the input
+ outputs = [gr.Image(type="numpy", label="Predicted bin"), gr.Textbox(label="Prediction 1"),
+ gr.Textbox(label="Probability"),
+ gr.Textbox(label="Prediction 2"), gr.Textbox(label="Probability"),
+ gr.Textbox(label="Prediction Time")],
+ # Adding a description
+ description="This application is an image recognition app that identifies recyclable objects and provides feedback on the appropriate bin for waste disposal.",
+ examples=[['images/bottle2.jpg'],
+ ['images/can2.jpg'],
+ ['images/wine2.jpg'],
+ ['images/compost2.jpg']],
+ cache_examples=False) # Disabling caching
+
+
+
+if __name__ == "__main__":
+ # Launch the interface
+ iface.launch(share=False)

predict.py

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+from keras.models import load_model
+from PIL import Image
+import numpy as np
+import requests
+from io import BytesIO
+import timeit
+import gdown
+import json
+import os
+
+# Define the class labels for the model's output
+
+# output_class = ["Plastic bottle/Can to deposit in Supermarkets", "Big Cardboard bin", "Unrecyclable garbage", "Glass - Purple bin", "Organic waste - Composter", "Grocery Packages - Orange bin", "Paper - Blue bin"]
+
+with open('Bins.json', 'r') as f:
+ bins_dict = json.load(f)
+ output_class = bins_dict["output_class"]
+
+url = "https://drive.google.com/uc?id=1TJSpf9wJYZSqIm5ytqXkU8RLuKJmmHHa"
+output = 'vgg16_model_after_one_epoch.h5'
+
+# check if path to file exist
+if not os.path.exists(output):
+ gdown.download(url, output, quiet=False)
+
+# Load the pre-trained model
+model = load_model(output)
+
+
+def predict(img_input, is_url=False):
+ """
+ Function to predict the class labels and probabilities of an input image.
+
+ Args:
+ file (str): Path to the input image file or URL.
+ is_url (bool): Indicates whether the file argument is a URL or local file path.
+
+ Returns:
+ dict: A dictionary containing the predicted class labels as keys and
+ their corresponding probabilities as values.
+ """
+ if isinstance(img_input, str):
+ if is_url is True:
+ # Download the image from the URL
+ response = requests.get(img_input)
+ img = Image.open(BytesIO(response.content))
+ else:
+ # Open the image file from the local file path
+ img = Image.open(img_input)
+ else:
+ img = img_input
+
+ # Resize the image to match the input size expected by the model (224x224)
+ resized_image = img.resize((224, 224))
+
+ # Normalize the pixel values to be between 0 and 1
+ normalized_image = np.array(resized_image) / 255.0
+
+ # Expand dimensions to match the input shape expected by the model
+ pred_image = np.expand_dims(normalized_image, axis=0)
+
+ # Make predictions using the model
+ predicted_array = model.predict(pred_image)
+
+ # Create a dictionary of predicted class labels and probabilities
+ pred_labels_and_probs = {
+ output_class[i]: float(predicted_array[0][i])
+ for i in range(len(output_class))
+ }
+
+ return pred_labels_and_probs
+
+
+def image_identity(image):
+ """
+ Take an image as input and return prediction information and an image of the predicted waste bin.
+
+ Parameters:
+ image (np.array): An image in numpy array format.
+
+ Returns:
+ bin_image1 (np.array): An image of the predicted waste bin.
+ predicted_class1 (str): The class of the predicted bin.
+ predicted_prob1 (float): The probability of the predicted class.
+ predicted_class2 (str): The second most likely class.
+ predicted_prob2 (float): The probability of the second most likely class.
+ prediction_time (str): The time taken to make the prediction.
+ """
+ image = Image.fromarray((image * 255).astype(np.uint8))
+
+ # Load the classification and image data from the JSON file
+ with open('Bins.json', 'r') as f:
+ bins_dict = json.load(f)
+
+ # Retrieve the class labels and bin images from the dictionary
+ output_class = bins_dict["output_class"]
+ bin_images = bins_dict["bin_images"]
+
+ # Record the start time before making predictions
+ start_time = timeit.default_timer()
+
+ # Make predictions on the provided image
+ predicted_class1, predicted_prob1, predicted_class2, predicted_prob2 = check_item(image)
+
+ # Record the end time after making predictions and calculate the total time taken
+ end_time = timeit.default_timer()
+ pred_time = round(end_time - start_time, 2)
+ prediction_time = f"{pred_time} seconds"
+
+ # Retrieve the image URL of the predicted bin
+ bin_1 = output_class.index(predicted_class1)
+ bin_image_url1 = bin_images[bin_1]
+ response1 = requests.get(bin_image_url1)
+
+ # Open the image and convert to a numpy array for returning
+ bin_image1 = Image.open(BytesIO(response1.content))
+ bin_image1 = np.array(bin_image1)
+
+ return bin_image1, predicted_class1, predicted_prob1, predicted_class2, predicted_prob2, prediction_time
+
+
+def check_item(file_path, is_url=False):
+ """
+ Function to make predictions on an input image and compare the results with the expected classes.
+
+ Args:
+ file_path (str): Path to the input image file or URL.
+ is_url (bool): Indicates whether the file argument is a URL or local file path.
+ """
+ # Obtain the predicted class labels and probabilities
+ prediction_dict = predict(file_path, is_url)
+
+ # Get the index of the highest prediction
+ ind = np.argmax(list(prediction_dict.values()))
+ predicted_class = list(prediction_dict.keys())[ind]
+ predicted_prob = list(prediction_dict.values())[ind]
+ predicted_prob = f"{predicted_prob:.2f}"
+ print(f"1st Prediction probabilities: {predicted_class} {predicted_prob}")
+
+ # Get the index of the second highest prediction
+ sorted_probs = np.sort(list(prediction_dict.values()))
+ ind2 = np.argsort(list(prediction_dict.values()))[-2]
+ predicted_class2 = list(prediction_dict.keys())[ind2]
+ predicted_prob2 = sorted_probs[-2]
+ predicted_prob2 = f"{predicted_prob2:.2f}"
+ print(f"2nd Prediction probabilities: {predicted_class2} {predicted_prob2}")
+ return predicted_class, predicted_prob, predicted_class2, predicted_prob2
+
+
+if __name__ == "__main__":
+ # Prompt the user to enter the file path to the input image
+ file_path = input("Enter the file path or URL to the input image: ")
+
+ # Prompt the user to indicate if the input is a URL or a local file path
+ is_url = input("Is the input a URL? (y/n): ").lower() == "y"
+
+ # Measure the time taken to make predictions
+ start_time = timeit.default_timer()
+
+ # Make predictions and compare with expected results
+ check_item(file_path, is_url)
+
+ # Calculate and print the prediction time
+ end_time = timeit.default_timer()
+ pred_time = round(end_time - start_time, 2)
+ print("Prediction time:", pred_time, "seconds")