Update app.py

app.py

@@ -1,141 +1,197 @@
 import os
+import smtplib
 import gradio as gr
 import tensorflow as tf
 import numpy as np
-import cv2
-import smtplib
-import ssl
 from email.message import EmailMessage
+from tensorflow.keras.preprocessing import image
 from PIL import Image
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
-from reportlab.lib.utils import ImageReader
-
-# Disable GPU to avoid CUDA errors
-os.environ["TF_ENABLE_ONEDNN_OPTS"] = "0"
-os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+from reportlab.lib import colors
+from reportlab.platypus import Table, TableStyle
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
 
-# Email sender credentials (Set your own credentials here)
-SENDER_EMAIL = "your_email@gmail.com"
-SENDER_PASSWORD = "your_email_password"
+# Read HTML content from `re.html`
+with open("templates/re.html", "r", encoding="utf-8") as file:
+    html_content = file.read()
 
+# List of sample images
+sample_images = [f"samples/{img}" for img in os.listdir("samples") if img.endswith((".png", ".jpg", ".jpeg"))]
+
+# Function to send email
 def send_email(receiver_email, file_path):
-    """Function to send an email with the generated PDF attached"""
+    sender_email = "[email protected]"
+    sender_password = "your_email_password"
+
     msg = EmailMessage()
-    msg["Subject"] = "Bone Fracture Patient Report"
-    msg["From"] = SENDER_EMAIL
+    msg["Subject"] = "Bone Fracture Detection Report"
+    msg["From"] = sender_email
     msg["To"] = receiver_email
-    msg.set_content("Please find the attached bone fracture report.")
+    msg.set_content("Please find attached your bone fracture detection report.")
 
-    # Attach PDF file
     with open(file_path, "rb") as f:
         file_data = f.read()
-        msg.add_attachment(file_data, maintype="application", subtype="pdf", filename="Fracture_Report.pdf")
+        file_name = os.path.basename(file_path)
+        msg.add_attachment(file_data, maintype="application", subtype="pdf", filename=file_name)
+
+    try:
+        with smtplib.SMTP_SSL("smtp.example.com", 465) as server:
+            server.login(sender_email, sender_password)
+            server.send_message(msg)
+        return "Report sent successfully."
+    except Exception as e:
+        return f"Error sending email: {e}"
+
+# Function to process X-ray and generate a PDF report
+def generate_report(name, age, gender, weight, height, allergies, cause, xray, email):
+    # Validate inputs
+    name = name[:50]
+    cause = " ".join(cause.split()[:100])  # Limit to 100 words
+
+    image_size = (224, 224)
+
+    def predict_fracture(xray_path):
+        img = Image.open(xray_path).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 fracture
+    prediction = predict_fracture(xray)
+    diagnosed_class = "Normal" if prediction > 0.5 else "Fractured"
+
+    # Injury severity classification
+    severity = "Mild" if prediction < 0.3 else "Moderate" if prediction < 0.7 else "Severe"
+
+    # Treatment details
+    treatment_data = [
+        ["Severity Level", "Recommended Treatment", "Recovery Duration"],
+        ["Mild", "Rest, pain relievers, follow-up X-ray", "4-6 weeks"],
+        ["Moderate", "Plaster cast, minor surgery if needed", "6-10 weeks"],
+        ["Severe", "Major surgery, metal implants, physiotherapy", "Several months"]
+    ]
 
-    # Send email
-    context = ssl.create_default_context()
-    with smtplib.SMTP_SSL("smtp.gmail.com", 465, context=context) as server:
-        server.login(SENDER_EMAIL, SENDER_PASSWORD)
-        server.send_message(msg)
-
-def preprocess_image(image):
-    """Preprocess the image for model prediction"""
-    image = cv2.cvtColor(np.array(image), cv2.COLOR_RGB2GRAY)
-    image = cv2.resize(image, (224, 224))
-    image = np.expand_dims(image, axis=-1)
-    image = np.expand_dims(image, axis=0) / 255.0
-    return image
-
-def generate_pdf(name, age, gender, weight, height, allergies, injury_cause, address, parent_name, image, email):
-    """Generate a PDF report"""
-    file_path = "Fracture_Report.pdf"
-    c = canvas.Canvas(file_path, pagesize=letter)
-    width, height = letter
-
-    # Title
-    c.setFont("Helvetica-Bold", 16)
-    c.drawCentredString(width / 2, height - 50, "Bone Fracture Patient Report")
-
-    # Patient Info Table
-    c.setFont("Helvetica", 12)
-    data = [
-        ["Patient Name:", name[:50]],
-        ["Age:", age],
-        ["Gender:", gender],
-        ["Weight (kg):", weight],
-        ["Height (cm):", height],
-        ["Allergies:", allergies[:100]],
-        ["Injury Cause:", " ".join(injury_cause.split()[:100])],  # Limit to 100 words
-        ["Address:", address[:100]],
-        ["Parent/Guardian Name:", parent_name[:50]],
+    # Cost & duration estimation
+    cost_duration_data = [
+        ["Hospital Type", "Estimated Cost", "Recovery Time"],
+        ["Government Hospital", f"₹{2000 if severity == 'Mild' else 8000 if severity == 'Moderate' else 20000} - ₹{5000 if severity == 'Mild' else 15000 if severity == 'Moderate' else 50000}", "4-12 weeks"],
+        ["Private Hospital", f"₹{10000 if severity == 'Mild' else 30000 if severity == 'Moderate' else 100000}+", "6 weeks - Several months"]
     ]
 
-    x_start, y_start = 50, height - 100
-    line_spacing = 20
+    # Save resized X-ray image
+    img = Image.open(xray).resize((300, 300))
+    img_path = f"{name}_xray.png"
+    img.save(img_path)
 
-    for row in data:
-        c.drawString(x_start, y_start, f"{row[0]} {row[1]}")
-        y_start -= line_spacing
+    # Generate PDF report
+    report_path = f"{name}_fracture_report.pdf"
+    c = canvas.Canvas(report_path, pagesize=letter)
+    
+    # Report title
+    c.setFont("Helvetica-Bold", 16)
+    c.drawCentredString(300, 770, "Bone Fracture Detection Report")
+
+    # Patient details
+    patient_data = [
+        ["Patient Name", name],
+        ["Age", age],
+        ["Gender", gender],
+        ["Weight", f"{weight} kg"],
+        ["Height", f"{height} cm"],
+        ["Allergies", allergies if allergies else "None"],
+        ["Cause of Injury", cause if cause else "Not Provided"],
+        ["Diagnosis", diagnosed_class],
+        ["Injury Severity", severity]
+    ]
 
-    # Add X-ray image
-    if image:
-        img = Image.open(image)
-        img.thumbnail((250, 250))
-        img_path = "temp_image.jpg"
-        img.save(img_path)
-        c.drawImage(ImageReader(img_path), width / 2 - 125, y_start - 250, 250, 250)
+    # Format and align tables
+    def format_table(data):
+        table = Table(data, colWidths=[270, 270])  # 90% width
+        table.setStyle(TableStyle([
+            ('BACKGROUND', (0, 0), (-1, 0), colors.darkblue),
+            ('TEXTCOLOR', (0, 0), (-1, 0), colors.whitesmoke),
+            ('ALIGN', (0, 0), (-1, -1), 'CENTER'),
+            ('FONTNAME', (0, 0), (-1, 0), 'Helvetica-Bold'),
+            ('BOTTOMPADDING', (0, 0), (-1, 0), 12),
+            ('GRID', (0, 0), (-1, -1), 1, colors.black),
+            ('VALIGN', (0, 0), (-1, -1), 'MIDDLE')
+        ]))
+        return table
+
+    # Draw patient details table
+    patient_table = format_table(patient_data)
+    patient_table.wrapOn(c, 480, 500)
+    patient_table.drawOn(c, 50, 620)
+
+    # Center X-ray image
+    c.drawInlineImage(img_path, 150, 320, width=300, height=300)
+    c.setFont("Helvetica-Bold", 12)
+    c.drawCentredString(300, 290, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}")
+
+    # Draw treatment & cost tables
+    treatment_table = format_table(treatment_data)
+    treatment_table.wrapOn(c, 480, 200)
+    treatment_table.drawOn(c, 50, 200)
+
+    cost_table = format_table(cost_duration_data)
+    cost_table.wrapOn(c, 480, 150)
+    cost_table.drawOn(c, 50, 80)
 
-    # Close and save the PDF
     c.save()
 
     # Send email
-    if email:
-        send_email(email, file_path)
-
-    return file_path
-
-def predict_and_generate_report(name, age, gender, weight, height, allergies, injury_cause, address, parent_name, image, email):
-    """Make a prediction and generate a report"""
-    if image is None:
-        return "Please upload an X-ray image."
-
-    # Preprocess and make a prediction
-    processed_image = preprocess_image(image)
-    prediction = model.predict(processed_image)
-    confidence = float(prediction[0][0]) * 100
-    fracture_status = "Yes" if confidence > 50 else "No"
-
-    # Generate PDF report
-    pdf_path = generate_pdf(name, age, gender, weight, height, allergies, injury_cause, address, parent_name, image, email)
-
-    return f"Fractured: {fracture_status} (Confidence: {confidence:.2f}%)", pdf_path
-
-# Define the Gradio Interface
-iface = gr.Interface(
-    fn=predict_and_generate_report,
-    inputs=[
-        gr.Textbox(label="Patient Name (Max 50 chars)"),
-        gr.Number(label="Age", precision=0),
-        gr.Radio(label="Gender", choices=["Male", "Female", "Other"]),
-        gr.Number(label="Weight (kg)"),
-        gr.Number(label="Height (cm)"),
-        gr.Textbox(label="Allergies (Max 100 chars)"),
-        gr.Textbox(label="Cause of Injury (Max 100 words)"),
-        gr.Textbox(label="Address (Max 100 chars)"),
-        gr.Textbox(label="Parent/Guardian Name (Max 50 chars)"),
-        gr.Image(type="pil", label="Upload X-ray Image"),
-        gr.Textbox(label="Email Address (for Report)"),
-    ],
-    outputs=[
-        gr.Textbox(label="Fracture Prediction"),
-        gr.File(label="Download Report"),
-    ],
-    title="Bone Fracture Detection System",
-    description="Upload an X-ray image, enter patient details, and generate a fracture report."
-)
-
+    email_status = send_email(email, report_path)
+
+    return report_path, email_status
+
+# Function to select a sample image
+def use_sample_image(sample_image_path):
+    return sample_image_path
+
+# Define Gradio Interface
+with gr.Blocks() as app:
+    gr.HTML(html_content)
+    gr.Markdown("## Bone Fracture Detection System")
+    
+    with gr.Row():
+        name = gr.Textbox(label="Patient Name", max_length=50)
+        age = gr.Number(label="Age")
+        gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
+    
+    with gr.Row():
+        weight = gr.Number(label="Weight (kg)")
+        height = gr.Number(label="Height (cm)")
+    
+    with gr.Row():
+        allergies = gr.Textbox(label="Allergies (if any)")
+        cause = gr.Textbox(label="Cause of Injury", max_lines=5)
+
+    with gr.Row():
+        email = gr.Textbox(label="Email Address")
+
+    with gr.Row():
+        xray = gr.Image(type="filepath", label="Upload X-ray Image")
+    
+    with gr.Row():
+        sample_selector = gr.Dropdown(choices=sample_images, label="Use Sample Image")
+        select_button = gr.Button("Load Sample Image")
+
+    submit_button = gr.Button("Generate Report")
+    output_file = gr.File(label="Download Report")
+    email_status = gr.Textbox(label="Email Status", interactive=False)
+
+    select_button.click(use_sample_image, inputs=[sample_selector], outputs=[xray])
+
+    submit_button.click(
+        generate_report,
+        inputs=[name, age, gender, weight, height, allergies, cause, xray, email],
+        outputs=[output_file, email_status],
+    )
+
+# Launch the Gradio app
 if __name__ == "__main__":
-    iface.launch()
+    app.launch()