import gradio as gr
from ultralytics import YOLO
import cv2
import numpy as np
from PIL import Image, ImageDraw, ImageFont
import sqlite3
import base64
from io import BytesIO
import tempfile
import pandas as pd
import os
from pathlib import Path

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

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 = "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)
        
        # 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}, Box: [{xmin}, {ymin}, {xmax}, {ymax}]")

    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)
    
    return pil_image_with_boxes, raw_predictions_str

# Function to add watermark
def add_watermark(image):
    try:
        logo = Image.open('image-logo.png').convert("RGBA")
        image = image.convert("RGBA")
        
        # Resize logo
        basewidth = 100
        wpercent = (basewidth / float(logo.size[0]))
        hsize = int((float(wpercent) * logo.size[1]))
        logo = logo.resize((basewidth, hsize), Image.LANCZOS)
        
        # Position logo
        position = (image.width - logo.width - 10, image.height - logo.height - 10)
        
        # Composite image
        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

# Function to add text and watermark
def add_text_and_watermark(image, name, age, medical_record, sex, label):
    draw = ImageDraw.Draw(image)
    
    # Load a larger font (adjust the size as needed)
    font_size = 48  # Example font size
    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}"
    
    # Calculate text bounding box
    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 a filled rectangle for the background
    draw.rectangle(
        [text_x - padding, text_y - padding, text_x + text_width + padding, text_y + text_height + padding],
        fill="black"
    )
    
    # Draw text on top of the rectangle
    draw.text((text_x, text_y), text, fill=(255, 255, 255, 255), font=font)

    # Add watermark to the image
    image_with_watermark = add_watermark(image)
    
    return image_with_watermark

# Function to initialize the database
def init_db():
    conn = sqlite3.connect('results.db')
    c = conn.cursor()
    c.execute('''CREATE TABLE IF NOT EXISTS results
                 (id INTEGER PRIMARY KEY, name TEXT, age INTEGER, medical_record INTEGER, sex TEXT, input_image BLOB, predicted_image BLOB, result TEXT)''')
    conn.commit()
    conn.close()

# Function to submit result to the database
def submit_result(name, age, medical_record, sex, input_image, predicted_image, result):
    conn = sqlite3.connect('results.db')
    c = conn.cursor()
    
    input_image_np = np.array(input_image)
    _, input_buffer = cv2.imencode('.png', cv2.cvtColor(input_image_np, cv2.COLOR_RGB2BGR))
    input_image_bytes = input_buffer.tobytes()
    
    predicted_image_np = np.array(predicted_image)
    predicted_image_rgb = cv2.cvtColor(predicted_image_np, cv2.COLOR_RGB2BGR)  # Ensure correct color conversion
    _, predicted_buffer = cv2.imencode('.png', predicted_image_rgb)
    predicted_image_bytes = predicted_buffer.tobytes()
    
    c.execute("INSERT INTO results (name, age, medical_record, sex, input_image, predicted_image, result) VALUES (?, ?, ?, ?, ?, ?, ?)", 
              (name, age, medical_record, sex, input_image_bytes, predicted_image_bytes, result))
    conn.commit()
    conn.close()
    return "Result submitted to database."

# Function to load and view database in HTML format
def view_database():
    conn = sqlite3.connect('results.db')
    c = conn.cursor()
    c.execute("SELECT name, age, medical_record, sex, input_image, predicted_image, result FROM results")
    rows = c.fetchall()
    conn.close()

    # Prepare the HTML content
    html_content = "<table border='1'><tr><th>Name</th><th>Age</th><th>Medical Record</th><th>Sex</th><th>Input Image</th><th>Predicted Image</th><th>Result</th></tr>"

    for row in rows:
        name, age, medical_record, sex, input_image_bytes, predicted_image_bytes, result = row

        # Decode the images
        input_image = Image.open(BytesIO(input_image_bytes))
        predicted_image = Image.open(BytesIO(predicted_image_bytes))

        # Convert images to base64 for display in HTML
        buffered_input = BytesIO()
        input_image.save(buffered_input, format="PNG")
        input_image_base64 = base64.b64encode(buffered_input.getvalue()).decode('utf-8')

        buffered_predicted = BytesIO()
        predicted_image.save(buffered_predicted, format="PNG")
        predicted_image_base64 = base64.b64encode(buffered_predicted.getvalue()).decode('utf-8')

        # Add a row to the HTML table
        html_content += f"<tr><td>{name}</td><td>{age}</td><td>{medical_record}</td><td>{sex}</td><td><img src='data:image/png;base64,{input_image_base64}' width='100'></td><td><img src='data:image/png;base64,{predicted_image_base64}' width='100'></td><td>{result}</td></tr>"

    html_content += "</table>"

    return html_content

# Function to download database or HTML file
def download_file(file_format):
    directory = tempfile.gettempdir()
    db_file_path = os.path.join(directory, 'results.db')
    
    if file_format == "Database (.db)":
        # Simulate creating or writing to the database file here
        if not os.path.exists(db_file_path):
            # Create a dummy database file for demonstration
            conn = sqlite3.connect(db_file_path)
            conn.execute('CREATE TABLE IF NOT EXISTS results (id INTEGER PRIMARY KEY, name TEXT)')
            conn.commit()
            conn.close()
        return db_file_path
    
    elif file_format == "HTML (.html)":
        # Connect to the SQLite database
        conn = sqlite3.connect(db_file_path)
        
        try:
            # Attempt to read the results table into a DataFrame
            df = pd.read_sql_query("SELECT * FROM results", conn)
        except pd.errors.DatabaseError as e:
            conn.close()
            raise ValueError("Table 'results' does not exist in the database.") from e
        
        # Close the database connection
        conn.close()

        # Define the path for the HTML file
        html_file_path = os.path.join(directory, "results.html")
        
        # Save the DataFrame as an HTML file
        df.to_html(html_file_path, index=False)

        return html_file_path
    else:
        raise ValueError("Invalid file format specified.")

# Initialize the database
init_db()

# Gradio Interface
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)
    submit_status = submit_result(name, age, medical_record, sex, input_image, output_image, raw_result)

    return output_image, raw_result, submit_status

# View Database Function (Updated)
def view_db_interface():
    html_content = view_database()
    return html_content

# Download Function
def download_interface(choice):
    try:
        # Get the file path
        file_path = download_file(choice)
        
        # Return the file path (string) directly for the Gradio component to handle
        return file_path
    except (FileNotFoundError, ValueError) as e:
        # Display error message in Gradio output
        return str(e)

# Gradio Blocks
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="Raw Result", lines=5)
        submit_status = gr.Textbox(label="Submission Status")

    submit_btn.click(fn=interface, inputs=[name, age, medical_record, sex, input_image], outputs=[output_image, raw_result, submit_status])

    with gr.Column():
        view_db_btn = gr.Button("View Database")
        db_output = gr.HTML(label="Database Records")

    view_db_btn.click(fn=view_db_interface, inputs=[], outputs=[db_output])

    with gr.Column():
        download_choice = gr.Radio(["Database (.db)", "Database (.html)"], label="Choose the file to download:")
        download_btn_db = gr.Button("Download Database (.db)")
        download_btn_html = gr.Button("Download HTML (.html)")
        download_file_output = gr.File(label="Download File")

    download_btn_db.click(lambda: download_file("Database (.db)"), outputs=download_file_output)
    download_btn_html.click(lambda: download_file("HTML (.html)"), outputs=download_file_output)

# Launch the Gradio app
demo.launch()