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import gradio as gr
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")
# Define image size based on the model's input requirement
image_size = (224, 224)
# Function to analyze injury severity
def analyze_injury(prediction):
if prediction < 0.3:
severity = "Mild"
treatment = "Rest, pain relievers, and follow-up X-ray."
gov_cost = "₹2,000 - ₹5,000"
private_cost = "₹10,000 - ₹20,000"
elif 0.3 <= prediction < 0.7:
severity = "Moderate"
treatment = "Plaster cast or splint; possible minor surgery."
gov_cost = "₹8,000 - ₹15,000"
private_cost = "₹30,000 - ₹60,000"
else:
severity = "Severe"
treatment = "Major surgery with metal implants, extensive physiotherapy."
gov_cost = "₹20,000 - ₹50,000"
private_cost = "₹1,00,000+"
return severity, treatment, gov_cost, private_cost
# Function to generate report
def generate_report(patient_name, age, gender, xray1, xray2):
# Process X-ray 1
img1 = Image.open(xray1).resize(image_size)
img_array1 = image.img_to_array(img1)
img_array1 = np.expand_dims(img_array1, axis=0) / 255.0
prediction1 = model.predict(img_array1)[0][0]
# Process X-ray 2
img2 = Image.open(xray2).resize(image_size)
img_array2 = image.img_to_array(img2)
img_array2 = np.expand_dims(img_array2, axis=0) / 255.0
prediction2 = model.predict(img_array2)[0][0]
# Get final analysis
avg_prediction = (prediction1 + prediction2) / 2
predicted_class = "Fractured" if avg_prediction > 0.5 else "Normal"
severity, treatment, gov_cost, private_cost = analyze_injury(avg_prediction)
# Generate PDF
report_path = f"{patient_name}_fracture_report.pdf"
c = canvas.Canvas(report_path, pagesize=letter)
c.setFont("Helvetica", 12)
c.drawString(100, 750, f"Patient Name: {patient_name}")
c.drawString(100, 730, f"Age: {age}")
c.drawString(100, 710, f"Gender: {gender}")
c.drawString(100, 690, f"Diagnosis: {predicted_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
# Define Gradio Interface
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 with treatment suggestions and cost estimates."
)
# Launch the Gradio app
if __name__ == "__main__":
interface.launch() |