from flask import Flask, render_template, request, send_file
import os
import tensorflow as tf
import numpy as np
from tensorflow.keras.preprocessing import image
from PIL import Image
from reportlab.lib.pagesizes import letter
from reportlab.pdfgen import canvas

# Load the trained model
model = tf.keras.models.load_model("my_keras_model.h5")

app = Flask(__name__, template_folder="templates", static_folder="static")

# Function to process X-rays and generate a PDF report
def generate_report(name, age, gender, xray1, xray2):
    image_size = (224, 224)

    def predict_fracture(xray):
        img = Image.open(xray).resize(image_size)
        img_array = image.img_to_array(img) / 255.0
        img_array = np.expand_dims(img_array, axis=0)
        prediction = model.predict(img_array)[0][0]
        return prediction

    # Predict on both X-rays
    prediction1 = predict_fracture(xray1)
    prediction2 = predict_fracture(xray2)
    avg_prediction = (prediction1 + prediction2) / 2
    diagnosed_class = "Fractured" if avg_prediction > 0.5 else "Normal"

    # Injury severity classification
    severity = "Mild" if avg_prediction < 0.3 else "Moderate" if avg_prediction < 0.7 else "Severe"
    treatment = {
        "Mild": "Rest, pain relievers, follow-up X-ray.",
        "Moderate": "Plaster cast, possible minor surgery.",
        "Severe": "Major surgery, metal implants, physiotherapy."
    }[severity]
    gov_cost = {"Mild": "₹2,000 - ₹5,000", "Moderate": "₹8,000 - ₹15,000", "Severe": "₹20,000 - ₹50,000"}[severity]
    private_cost = {"Mild": "₹10,000 - ₹20,000", "Moderate": "₹30,000 - ₹60,000", "Severe": "₹1,00,000+"}[severity]

    # Generate PDF report
    report_path = f"{name}_fracture_report.pdf"
    c = canvas.Canvas(report_path, pagesize=letter)
    c.setFont("Helvetica", 12)
    c.drawString(100, 750, f"Patient Name: {name}")
    c.drawString(100, 730, f"Age: {age}")
    c.drawString(100, 710, f"Gender: {gender}")
    c.drawString(100, 690, f"Diagnosis: {diagnosed_class}")
    c.drawString(100, 670, f"Injury Severity: {severity}")
    c.drawString(100, 650, f"Recommended Treatment: {treatment}")
    c.drawString(100, 630, f"Estimated Cost (Govt Hospital): {gov_cost}")
    c.drawString(100, 610, f"Estimated Cost (Private Hospital): {private_cost}")
    c.save()

    return report_path  # Return path for auto-download

# Flask Route: Serve HTML Page
@app.route("/")
def home():
    return render_template("re.html")

# Flask Route: Handle Form Submission
@app.route("/submit_report", methods=["POST"])
def submit_report():
    name = request.form["first_name"] + " " + request.form["surname"]
    age = request.form["age"]
    gender = request.form["gender"]
    xray1 = request.files["xray_side"]
    xray2 = request.files["xray_top"]

    # Generate PDF report
    pdf_path = generate_report(name, age, gender, xray1, xray2)

    return send_file(pdf_path, as_attachment=True)  # Auto-download report

if __name__ == "__main__":
    app.run(host="0.0.0.0", port=7860, debug=False)  # Run Flask on 7860