File size: 3,863 Bytes
03486e0 f494b68 1338706 03486e0 f494b68 03486e0 f494b68 03486e0 8d0869a 1338706 03486e0 f494b68 03486e0 8d0869a 03486e0 48e0944 f494b68 1338706 c716ed3 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 |
from flask import Flask, render_template, request, send_file
import gradio as gr
import threading
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
import os
# Load the trained model
model = tf.keras.models.load_model("my_keras_model.h5")
app = Flask(__name__, template_folder="templates", static_folder="static") # Ensure correct paths
# 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
# Run Gradio in a separate thread
def run_gradio():
interface = gr.Interface(
fn=generate_report,
inputs=[
gr.Textbox(label="Patient Name"),
gr.Number(label="Age"),
gr.Radio(["Male", "Female", "Other"], label="Gender"),
gr.Image(type="file", label="Upload X-ray Image 1"),
gr.Image(type="file", label="Upload X-ray Image 2"),
],
outputs=gr.File(label="Download Report"),
title="Bone Fracture Detection & Medical Report",
description="Enter patient details, upload two X-ray images, and generate a detailed medical report."
)
interface.launch(share=True)
if __name__ == "__main__":
threading.Thread(target=run_gradio).start()
app.run(host="0.0.0.0", port=7861, debug=True) # Flask runs separately |