File size: 3,445 Bytes
c846681 8a25800 c846681 572837a c846681 8a25800 c846681 8a25800 c846681 8a25800 c846681 8a25800 c846681 8a25800 c846681 |
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 |
import gradio as gr
from tensorflow import keras
import numpy as np
from PIL import Image
from fpdf import FPDF # Import library to generate PDF
# Load the pre-trained Keras model
model = keras.models.load_model('retino_model.keras')
# Define class names for your model predictions
class_names = ['Healthy', 'Mild DR', 'Moderate DR', 'Proliferative DR', 'Severe DR']
# Function to provide additional care information based on the predicted condition
def eye_care_recommendations(predicted_class):
recommendations = {
'Healthy': 'Your eyes seem healthy. Continue with regular eye check-ups and maintain a balanced diet.',
'Mild DR': 'Mild signs of diabetic retinopathy. Ensure strict blood sugar control and regular eye exams.',
'Moderate DR': 'Moderate diabetic retinopathy detected. Consult with an ophthalmologist for treatment options.',
'Proliferative DR': 'Advanced stage detected. Immediate medical attention is required to prevent further vision loss.',
'Severe DR': 'Severe diabetic retinopathy detected. Medical intervention is necessary. Please visit a doctor immediately.'
}
return recommendations.get(predicted_class, "No recommendation available.")
# Function to generate a PDF report
def generate_pdf(patient_info, prediction, care_info):
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=12)
pdf.cell(200, 10, txt="Diabetic Retinopathy Prediction Report", ln=True, align='C')
pdf.ln(10)
pdf.cell(200, 10, txt=f"Patient Name: {patient_info['name']}", ln=True)
pdf.cell(200, 10, txt=f"Age: {patient_info['age']}", ln=True)
pdf.ln(10)
pdf.cell(200, 10, txt=f"Prediction: {prediction}", ln=True)
pdf.cell(200, 10, txt=f"Eye Care Recommendations: {care_info}", ln=True)
# Save PDF
pdf_output = f"{patient_info['name']}_report.pdf"
pdf.output(pdf_output)
return pdf_output
# Prediction function that processes the image and returns the result and care advice
def predict(image, name, age):
# Resize image to the expected size for the model
image = image.resize((128, 128)) # Adjust this size based on your model's input size
image = np.expand_dims(np.array(image), axis=0) # Add batch dimension
# Make a prediction
predictions = model.predict(image)
predicted_class_index = np.argmax(predictions, axis=1)[0]
predicted_class = class_names[predicted_class_index]
# Get eye care recommendations based on the prediction
care_info = eye_care_recommendations(predicted_class)
# Generate a PDF report
patient_info = {'name': name, 'age': age}
pdf_report = generate_pdf(patient_info, predicted_class, care_info)
# Return the prediction, care information, and PDF link
return f"Predicted Condition: {predicted_class}", care_info, pdf_report
# Gradio interface
interface = gr.Interface(
fn=predict,
inputs=[gr.Image(type="pil"), gr.Textbox(label="Patient Name"), gr.Textbox(label="Age")],
outputs=[gr.Textbox(label="Prediction"), gr.Textbox(label="Eye Care Recommendations"), gr.File(label="Download Report (PDF)")],
title="Diabetic Retinopathy Prediction",
description="Upload a retinal image, enter patient information, and the model will predict the stage of Diabetic Retinopathy. Eye care recommendations and a PDF report will be provided."
)
# Launch the interface
if __name__ == "__main__":
interface.launch()
|