import gradio as gr from tensorflow.keras.models import load_model from tensorflow.keras.preprocessing.image import img_to_array import numpy as np from PIL import Image import matplotlib.pyplot as plt import csv import os from datetime import datetime # Load model model = load_model("waste_classification(Mobilenetv2).h5", compile=False) class_names = ['cardboard', 'glass', 'metal', 'paper', 'plastic', 'trash'] # CSV header csv_file = "predictions.csv" if not os.path.exists(csv_file): with open(csv_file, mode='w', newline='') as file: writer = csv.writer(file) writer.writerow(["timestamp", "predicted_class", "confidence", "probabilities", "source"]) # Prediction + save to CSV def predict_with_chart(image): if image is None: return "No image received", None image = image.resize((224, 224)) img_array = img_to_array(image) / 255.0 img_array = np.expand_dims(img_array, axis=0) prediction = model.predict(img_array)[0] pred_index = np.argmax(prediction) pred_label = class_names[pred_index] confidence = float(np.max(prediction)) # Log prediction to CSV with open(csv_file, mode='a', newline='') as file: writer = csv.writer(file) writer.writerow([ datetime.now().strftime('%Y-%m-%d %H:%M:%S'), pred_label, round(confidence, 4), [round(p, 4) for p in prediction.tolist()], "upload_or_webcam" ]) # Plot class probabilities fig, ax = plt.subplots(figsize=(6, 4)) ax.bar(class_names, prediction, color='skyblue') ax.set_ylabel('Probability') ax.set_ylim(0, 1) ax.set_title('Class Probabilities') plt.xticks(rotation=45) plt.tight_layout() return f"Prediction: {pred_label} ({confidence*100:.1f}%)", fig # Gradio UI with gr.Blocks() as demo: gr.Markdown("## 🗑️ Waste Classifier — Upload or Webcam\nAutomatically logs predictions to CSV.") with gr.Row(): image_input = gr.Image(type="pil", label="Upload or Webcam (click camera icon)") with gr.Row(): label_output = gr.Textbox(label="Predicted Class") plot_output = gr.Plot(label="Class Probability Chart") image_input.change(fn=predict_with_chart, inputs=image_input, outputs=[label_output, plot_output]) demo.launch()