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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 |