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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()
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