Update app.py

app.py

@@ -8,6 +8,8 @@ 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
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
@@ -17,7 +19,7 @@ with open("templates/re.html", "r", encoding="utf-8") as file:
     html_content = file.read()
 
 # Function to process X-rays and generate a PDF report
-def generate_report(name, age, gender, xray1, xray2):
+def generate_report(name, age, gender, allergies, cause, xray1, xray2):
     image_size = (224, 224)
 
     def predict_fracture(xray_path):
@@ -35,26 +37,68 @@ def generate_report(name, age, gender, xray1, xray2):
 
     # 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]
+    treatment_details = {
+        "Mild": "Your fracture is classified as **Mild**. It may heal with rest, pain relievers, and a follow-up X-ray. Avoid excessive movement of the affected area.",
+        "Moderate": "Your fracture is classified as **Moderate**. You may require a plaster cast, splint, or minor surgery. Recovery takes **4-8 weeks**.",
+        "Severe": "Your fracture is classified as **Severe**. Surgery with metal implants and extensive physiotherapy is required. Recovery takes **several months** with proper rehabilitation."
+    }
+    treatment = treatment_details[severity]
+
+    # Estimated cost & duration
+    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 X-ray images for report
+    img1 = Image.open(xray1).resize((300, 300))
+    img2 = Image.open(xray2).resize((300, 300))
+    img1_path = f"{name}_xray1.png"
+    img2_path = f"{name}_xray2.png"
+    img1.save(img1_path)
+    img2.save(img2_path)
 
     # Generate PDF report
     report_path = f"{name}_fracture_report.pdf"
     c = canvas.Canvas(report_path, pagesize=letter)
+    c.setFont("Helvetica-Bold", 14)
+    c.drawString(200, 770, "Bone Fracture Detection Report")
+
+    # Patient details
     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.drawString(100, 740, f"Patient Name: {name}")
+    c.drawString(100, 720, f"Age: {age}")
+    c.drawString(100, 700, f"Gender: {gender}")
+    c.drawString(100, 680, f"Allergies: {allergies if allergies else 'None'}")
+    c.drawString(100, 660, f"Cause of Injury: {cause if cause else 'Not Provided'}")
+
+    # Diagnosis
+    c.setFont("Helvetica-Bold", 12)
+    c.drawString(100, 630, "Diagnosis & Treatment Plan:")
+    c.setFont("Helvetica", 11)
+    c.drawString(100, 610, f"Fracture Detected: {diagnosed_class}")
+    c.drawString(100, 590, f"Injury Severity: {severity}")
+    c.setFont("Helvetica", 10)
+    c.drawString(100, 570, f"{treatment}")
+
+    # Load and insert X-ray images
+    c.drawInlineImage(img1_path, 50, 250, width=250, height=250)
+    c.drawInlineImage(img2_path, 320, 250, width=250, height=250)
+
+    # Cost estimation table
+    table = Table(cost_duration_data)
+    table.setStyle(TableStyle([
+        ('BACKGROUND', (0, 0), (-1, 0), colors.grey),
+        ('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)
+    ]))
+    table.wrapOn(c, 400, 300)
+    table.drawOn(c, 100, 150)
+
     c.save()
 
     return report_path  # Return path for auto-download
@@ -69,6 +113,10 @@ with gr.Blocks() as app:
         age = gr.Number(label="Age")
         gender = gr.Radio(["Male", "Female", "Other"], label="Gender")
 
+    with gr.Row():
+        allergies = gr.Textbox(label="Allergies (if any)")
+        cause = gr.Textbox(label="Cause of Injury")
+
     with gr.Row():
         xray1 = gr.Image(type="filepath", label="Upload X-ray Image 1")
         xray2 = gr.Image(type="filepath", label="Upload X-ray Image 2")
@@ -78,7 +126,7 @@ with gr.Blocks() as app:
 
     submit_button.click(
         generate_report,
-        inputs=[name, age, gender, xray1, xray2],
+        inputs=[name, age, gender, allergies, cause, xray1, xray2],
         outputs=[output_file],
     )