app.py

import gradio as gr
from ultralytics import YOLO
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import base64
from io import BytesIO
import tempfile
import os
from pathlib import Path
import shutil
from openpyxl import Workbook, load_workbook

# Load YOLOv8 model
model = YOLO("best.pt")

# Create directories if not present
uploaded_folder = Path('Uploaded_Picture')
predicted_folder = Path('Predicted_Picture')
uploaded_folder.mkdir(parents=True, exist_ok=True)
predicted_folder.mkdir(parents=True, exist_ok=True)

# Path for Excel database file
xlsx_db_file = Path('patient_predictions.xlsx')

# Initialize Excel database file if not present
if not xlsx_db_file.exists():
    workbook = Workbook()
    sheet = workbook.active
    sheet.title = "Predictions"
    sheet.append(["Name", "Age", "Medical Record", "Sex", "Result", "Image Path"])
    workbook.save(xlsx_db_file)

def predict_image(input_image, name, age, medical_record, sex):
    if input_image is None:
        return None, "Please Input The Image"

    # Convert Gradio input image (PIL Image) to numpy array
    image_np = np.array(input_image)

    # Ensure the image is in the correct format
    if len(image_np.shape) == 2:  # grayscale to RGB
        image_np = cv2.cvtColor(image_np, cv2.COLOR_GRAY2RGB)
    elif image_np.shape[2] == 4:  # RGBA to RGB
        image_np = cv2.cvtColor(image_np, cv2.COLOR_RGBA2RGB)

    # Perform prediction
    results = model(image_np)

    # Draw bounding boxes on the image
    image_with_boxes = image_np.copy()
    raw_predictions = []

    if results[0].boxes:
        # Sort the results by confidence and take the highest confidence one
        highest_confidence_result = max(results[0].boxes, key=lambda x: x.conf.item())

        # Determine the label based on the class index
        class_index = highest_confidence_result.cls.item()
        if class_index == 0:
            label = "Cataract"
            color = (255, 0, 0)
        elif class_index == 1:
            label = "Normal"
            color = (0, 255, 0)

        confidence = highest_confidence_result.conf.item()
        xmin, ymin, xmax, ymax = map(int, highest_confidence_result.xyxy[0])

        # Calculate the average of box width and height
        box_width = xmax - xmin
        box_height = ymax - ymin
        avg_dimension = (box_width + box_height) / 2
        
        # Calculate the circle radius as 1/12 of the average dimension
        radius = int(avg_dimension / 12)
        
        # Calculate the center of the bounding box
        center_x = int((xmin + xmax) / 2)
        center_y = int((ymin + ymax) / 2)
        
        # Draw the circle at the center of the bounding box with the color corresponding to the label
        cv2.circle(image_with_boxes, (center_x, center_y), radius, color, 2)

        # Enlarge font scale and thickness
        font_scale = 1.0
        thickness = 2
        
        # Calculate label background size
        (text_width, text_height), baseline = cv2.getTextSize(f'{label} {confidence:.2f}', cv2.FONT_HERSHEY_SIMPLEX, font_scale, thickness)
        cv2.rectangle(image_with_boxes, (xmin, ymin - text_height - baseline), (xmin + text_width, ymin), (0, 0, 0), cv2.FILLED)
        
        # Put the label text with black background
        cv2.putText(image_with_boxes, f'{label} {confidence:.2f}', (xmin, ymin - 10), cv2.FONT_HERSHEY_SIMPLEX, font_scale, (255, 255, 255), thickness)
        
        raw_predictions.append(f"Label: {label}, Confidence: {confidence:.2f}, Circle Center: [{center_x}, {center_y}], Radius: {radius}")

    raw_predictions_str = "\n".join(raw_predictions)
    
    # Convert to PIL image for further processing
    pil_image_with_boxes = Image.fromarray(image_with_boxes)

    # Add text and watermark
    pil_image_with_boxes = add_text_and_watermark(pil_image_with_boxes, name, age, medical_record, sex, label)
    
    # Save images to directories
    image_name = f"{name}-{age}-{sex}-{medical_record}.png"
    input_image.save(uploaded_folder / image_name)
    pil_image_with_boxes.save(predicted_folder / image_name)
    
    # Convert the predicted image to base64 for embedding in the XLSX file
    buffered = BytesIO()
    pil_image_with_boxes.save(buffered, format="PNG")
    predicted_image_base64 = base64.b64encode(buffered.getvalue()).decode()

    # Append the prediction to the XLSX database
    append_patient_info_to_xlsx(name, age, medical_record, sex, label, image_name)
    
    return pil_image_with_boxes, raw_predictions_str

def add_watermark(image):
    try:
        logo = Image.open('image-logo.png').convert("RGBA")
        image = image.convert("RGBA")
        basewidth = 100
        wpercent = (basewidth / float(logo.size[0]))
        hsize = int((float(wpercent) * logo.size[1]))
        logo = logo.resize((basewidth, hsize), Image.LANCZOS)
        position = (image.width - logo.width - 10, image.height - logo.height - 10)
        transparent = Image.new('RGBA', (image.width, image.height), (0, 0, 0, 0))
        transparent.paste(image, (0, 0))
        transparent.paste(logo, position, mask=logo)
        return transparent.convert("RGB")
    except Exception as e:
        print(f"Error adding watermark: {e}")
        return image

def add_text_and_watermark(image, name, age, medical_record, sex, label):
    draw = ImageDraw.Draw(image)
    font_size = 24
    try:
        font = ImageFont.truetype("font.ttf", size=font_size)
    except IOError:
        font = ImageFont.load_default()
        print("Error: cannot open resource, using default font.")

    text = f"Name: {name}, Age: {age}, Medical Record: {medical_record}, Sex: {sex}, Result: {label}"
    text_bbox = draw.textbbox((0, 0), text, font=font)
    text_width, text_height = text_bbox[2] - text_bbox[0], text_bbox[3] - text_bbox[1]
    text_x = 20
    text_y = 40
    padding = 10
    draw.rectangle([text_x - padding, text_y - padding, text_x + text_width + padding, text_y + text_height + padding], fill="black")
    draw.text((text_x, text_y), text, fill=(255, 255, 255, 255), font=font)

    image_with_watermark = add_watermark(image)
    return image_with_watermark

def append_patient_info_to_xlsx(name, age, medical_record, sex, result, image_path):
    if not xlsx_db_file.exists():
        workbook = Workbook()
        sheet = workbook.active
        sheet.title = "Predictions"
        sheet.append(["Name", "Age", "Medical Record", "Sex", "Result", "Image Path"])
        workbook.save(xlsx_db_file)
    
    workbook = load_workbook(xlsx_db_file)
    sheet = workbook.active
    sheet.append([name, age, medical_record, sex, result, str(image_path)])
    workbook.save(xlsx_db_file)

    return str(xlsx_db_file)

def download_folder(folder):
    zip_path = os.path.join(tempfile.gettempdir(), f"{folder}.zip")
    shutil.make_archive(zip_path.replace('.zip', ''), 'zip', folder)
    return zip_path

def interface(name, age, medical_record, sex, input_image):
    if input_image is None:
        return None, "Please upload an image.", None

    output_image, raw_result = predict_image(input_image, name, age, medical_record, sex)
    
    return output_image, raw_result, str(xlsx_db_file)

def download_predicted_folder():
    return download_folder(predicted_folder)

def download_uploaded_folder():
    return download_folder(uploaded_folder)

with gr.Blocks() as demo:
    with gr.Column():
        gr.Markdown("# Cataract Detection System")
        gr.Markdown("Upload an image to detect cataract and add patient details.")
        gr.Markdown("This application uses YOLOv10 with mAP=0.994")
    
    with gr.Column():
        name = gr.Textbox(label="Name")
        age = gr.Number(label="Age")
        medical_record = gr.Number(label="Medical Record")
        sex = gr.Radio(["Male", "Female"], label="Sex")
        input_image = gr.Image(type="pil", label="Upload an Image", image_mode="RGB")

    with gr.Column():
        submit_btn = gr.Button("Submit")
        output_image = gr.Image(type="pil", label="Predicted Image")

    with gr.Row():
        raw_result = gr.Textbox(label="Prediction Result")
    
    with gr.Row():
        download_xlsx_btn = gr.Button("Download Patient Information (XLSX)")
        download_uploaded_btn = gr.Button("Download Uploaded Images")
        download_predicted_btn = gr.Button("Download Predicted Images")
    
    xlsx_file = gr.File(label="Patient Information XLSX File")
    uploaded_folder_file = gr.File(label="Uploaded Images Zip File")
    predicted_folder_file = gr.File(label="Predicted Images Zip File")

    submit_btn.click(fn=interface, inputs=[name, age, medical_record, sex, input_image], outputs=[output_image, raw_result, xlsx_file])
    download_xlsx_btn.click(fn=lambda: str(xlsx_db_file), outputs=xlsx_file)
    download_uploaded_btn.click(fn=download_uploaded_folder, outputs=uploaded_folder_file)
    download_predicted_btn.click(fn=download_predicted_folder, outputs=predicted_folder_file)

demo.launch()