Update app.py

app.py

@@ -3,54 +3,55 @@ import smtplib
 import gradio as gr
 import tensorflow as tf
 import numpy as np
-from email.message import EmailMessage
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
 from tensorflow.keras.preprocessing import image
 from PIL import Image
 from reportlab.lib.pagesizes import letter
 from reportlab.pdfgen import canvas
-from reportlab.lib import colors
-from reportlab.platypus import Table, TableStyle
+from reportlab.lib.utils import simpleSplit
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
 
-# 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
 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):
-    sender_email = "your_email@example.com"
+# Email function
+def send_email(patient_email, patient_name, pdf_path):
+    sender_email = "your_email@gmail.com"
     sender_password = "your_email_password"
-
-    msg = EmailMessage()
-    msg["Subject"] = "Bone Fracture Detection Report"
-    msg["From"] = sender_email
-    msg["To"] = receiver_email
-    msg.set_content("Please find attached your bone fracture detection report.")
-
-    with open(file_path, "rb") as f:
-        file_data = f.read()
-        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."
+        msg = MIMEMultipart()
+        msg["From"] = sender_email
+        msg["To"] = patient_email
+        msg["Subject"] = "Your Bone Fracture Report"
+
+        body = f"Hello {patient_name},\n\nPlease find attached your bone fracture detection report from XYZ Hospital.\n\nBest regards,\nXYZ Hospital"
+        msg.attach(MIMEText(body, "plain"))
+
+        with open(pdf_path, "rb") as attachment:
+            part = MIMEBase("application", "octet-stream")
+            part.set_payload(attachment.read())
+            encoders.encode_base64(part)
+            part.add_header("Content-Disposition", f"attachment; filename={pdf_path}")
+            msg.attach(part)
+
+        server = smtplib.SMTP("smtp.gmail.com", 587)
+        server.starttls()
+        server.login(sender_email, sender_password)
+        server.sendmail(sender_email, patient_email, msg.as_string())
+        server.quit()
+
+        return "Report sent successfully!"
     except Exception as e:
-        return f"Error sending email: {e}"
+        return f"Error sending email: {str(e)}"
 
-# Function to process X-ray and generate a PDF report
+# Generate 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):
@@ -60,138 +61,125 @@ def generate_report(name, age, gender, weight, height, allergies, cause, xray, e
         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"]
-    ]
-
-    # 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"]
-    ]
-
-    # Save resized X-ray image
+    # Save X-ray image
     img = Image.open(xray).resize((300, 300))
     img_path = f"{name}_xray.png"
     img.save(img_path)
 
-    # Generate PDF report
+    # PDF Report
     report_path = f"{name}_fracture_report.pdf"
     c = canvas.Canvas(report_path, pagesize=letter)
-    
-    # Report title
+
+    # Header
     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]
+    c.drawCentredString(300, 770, "XYZ Hospital, New Delhi")
+    c.setFont("Helvetica", 12)
+    c.drawCentredString(300, 750, "123 Health Street, New Delhi, India")
+    c.line(50, 740, 550, 740)
+
+    # Patient Details
+    c.setFont("Helvetica-Bold", 14)
+    c.drawString(50, 710, "Patient Information:")
+    c.setFont("Helvetica", 12)
+    details = [
+        f"Name: {name}",
+        f"Age: {age}",
+        f"Gender: {gender}",
+        f"Weight: {weight} kg",
+        f"Height: {height} cm",
+        f"Allergies: {allergies if allergies else 'None'}",
+        f"Cause of Injury: {cause if cause else 'Not Provided'}"
     ]
-
-    # 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)
+    y = 690
+    for detail in details:
+        c.drawString(50, y, detail)
+        y -= 20
+
+    # Diagnosis
+    c.setFont("Helvetica-Bold", 14)
+    c.drawString(50, y, "Diagnosis:")
+    c.setFont("Helvetica", 12)
+    y -= 20
+    c.drawString(50, y, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}")
+    y -= 20
+    c.drawString(50, y, f"Injury Severity: {severity}")
+
+    # X-ray Image
+    c.drawInlineImage(img_path, 150, y - 260, width=300, height=300)
+    y -= 280
+
+    # Treatment & Recommendations
+    c.setFont("Helvetica-Bold", 14)
+    c.drawString(50, y, "Recommended Treatment:")
+    c.setFont("Helvetica", 12)
+    y -= 20
+    recommendations = {
+        "Mild": "Rest, pain relievers, and follow-up X-ray.",
+        "Moderate": "Plaster cast, minor surgery if needed.",
+        "Severe": "Major surgery, metal implants, and physiotherapy."
+    }
+    treatment_text = recommendations[severity]
+    for line in simpleSplit(treatment_text, "Helvetica", 12, 480):
+        c.drawString(50, y, line)
+        y -= 20
+
+    # Estimated Cost
+    c.setFont("Helvetica-Bold", 14)
+    c.drawString(50, y, "Estimated Treatment Cost:")
+    c.setFont("Helvetica", 12)
+    y -= 20
+    cost_gov = f"Government Hospital: ₹{2000 if severity == 'Mild' else 8000 if severity == 'Moderate' else 20000} - ₹{5000 if severity == 'Mild' else 15000 if severity == 'Moderate' else 50000}"
+    cost_priv = f"Private Hospital: ₹{10000 if severity == 'Mild' else 30000 if severity == 'Moderate' else 100000}+"
+    for line in simpleSplit(cost_gov, "Helvetica", 12, 480):
+        c.drawString(50, y, line)
+        y -= 20
+    for line in simpleSplit(cost_priv, "Helvetica", 12, 480):
+        c.drawString(50, y, line)
+        y -= 20
 
     c.save()
 
-    # Send email
-    email_status = send_email(email, report_path)
+    # Send email with report
+    email_status = send_email(email, name, 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
+# Gradio Interface
 with gr.Blocks() as app:
-    gr.HTML(html_content)
-    gr.Markdown("## Bone Fracture Detection System")
-    
+    gr.Markdown("# Bone Fracture Detection System\n### AI-powered diagnosis and treatment recommendations")
+
     with gr.Row():
-        name = gr.Textbox(label="Patient Name", max_length=50)
+        name = gr.Textbox(label="Patient Name", max_chars=50)
         age = gr.Number(label="Age")
+
+    with gr.Row():
         gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
-    
+        email = gr.Textbox(label="Patient Email")
+
     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")
+        allergies = gr.Textbox(label="Allergies (if any)")
+        cause = gr.Textbox(label="Cause of Injury (Max 100 words)", max_chars=500)
 
     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],
+        outputs=[output_file, email_status]
     )
 
-# Launch the Gradio app
 if __name__ == "__main__":
     app.launch()