import gradio as gr
import numpy as np
import pandas as pd
from PIL import Image, ImageDraw, ImageFont
from ultralytics import YOLO

# Load YOLO model
model = YOLO('trash_detection.pt')

def predict(image):
    """
    Function to make predictions using YOLO model.
    Args:
        image (PIL.Image): Input image.
    Returns:
        Tuple: Processed image with bounding boxes and a list of predictions with labels, confidence, and bounding boxes.
    """
    # Convert PIL image to numpy array
    img = np.array(image)

    results = model(img)  # Run the model on the image
    if isinstance(results, list):
        results = results[0]  # Take the first result if it's a list

    # Access the bounding boxes and other relevant information
    boxes = results[0].boxes  # Get the boxes from the first result

    # Convert the boxes to a Pandas DataFrame
    df = pd.DataFrame(boxes.xyxy.cpu().numpy(), columns=['x1', 'y1', 'x2', 'y2'])
    df['confidence'] = boxes.conf.cpu().numpy()
    df['class'] = boxes.cls.cpu().numpy()

    # Get the class names
    class_names = results.names  # Class names dictionary
    df['label'] = df['class'].apply(lambda x: class_names[int(x)])

    # Select the necessary columns for the output
    df_output = df[['label', 'confidence', 'x1', 'y1', 'x2', 'y2']]

    # Convert DataFrame to list of lists
    output = df_output.values.tolist()

    # Draw bounding boxes on the image
    pil_img = Image.fromarray(img)  # Convert numpy array back to PIL image
    draw = ImageDraw.Draw(pil_img)

    # Load a font with a larger size
    try:
        font = ImageFont.truetype("/Library/Fonts/Arial.ttf", 24)  # Adjust the font size as needed
    except IOError:
        font = ImageFont.load_default()  # Use default font if the specified one is not found

    for _, row in df.iterrows():
        x1, y1, x2, y2 = row['x1'], row['y1'], row['x2'], row['y2']
        label = row['label']
        confidence = row['confidence']

        # Draw bounding box
        draw.rectangle([x1, y1, x2, y2], outline="red", width=3)

        # Draw label and confidence with larger font
        text = f"{label} ({confidence:.2f})"
        draw.text((x1, y1 - 30), text, font=font, fill="red")

    # Return the image with bounding boxes and the prediction results
    return pil_img, output

# Create Gradio interface
iface = gr.Interface(
    fn=predict,
    inputs=gr.Image(type="pil"),  # Input image as PIL
    outputs=[gr.Image(type="pil"),  # Output image with bounding boxes
             gr.Dataframe(
                 headers=["Label", "Confidence", "Xmin", "Ymin", "Xmax", "Ymax"],
                 label="Predictions"
             )],
    title="Garbage Detection",
    description="Upload an image to detect objects."
)

# Launch the app
if __name__ == "__main__":
    iface.launch(share=True)