app0.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:
        highest_confidence_result = max(results[0].boxes, key=lambda x: x.conf.item())

        class_index = highest_confidence_result.cls.item()
        if class_index == 0:
            label = "Immature"
            color = (0, 255, 255)  # Yellow for Immature
        elif class_index == 1:
            label = "Mature"
            color = (255, 0, 0)  # Red for Mature
        else:
            label = "Normal"
            color = (0, 255, 0)  # Green for Normal

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

        # Draw the bounding box
        cv2.rectangle(image_with_boxes, (xmin, ymin), (xmax, ymax), color, 2)

        font_scale = 1.0
        thickness = 2
        (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)
        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}, Box: [{xmin}, {ymin}, {xmax}, {ymax}]")

    raw_predictions_str = "\n".join(raw_predictions)

    pil_image_with_boxes = Image.fromarray(image_with_boxes)
    pil_image_with_boxes = add_text_and_watermark(pil_image_with_boxes, name, age, medical_record, sex, label)

    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)

    append_patient_info_to_xlsx(name, age, medical_record, sex, label, predicted_folder / 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 YOLOv8 with mAP=0.981")
    
    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()