Update app.py

app.py

@@ -3,9 +3,9 @@ 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
-from fpdf import FPDF
-import datetime
 
 # Load the trained model
 model = tf.keras.models.load_model("my_keras_model.h5")
@@ -13,56 +13,61 @@ 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 make predictions
-def predict_image(img):
-    img = img.resize(image_size)  # Resize image to model's expected size
-    img_array = image.img_to_array(img)
-    img_array = np.expand_dims(img_array, axis=0) / 255.0  # Normalize
-    prediction = model.predict(img_array)
+# 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
 
-    # Assuming binary classification (fractured or normal)
-    class_names = ['Fractured', 'Normal']
-    predicted_class = class_names[int(prediction[0] > 0.5)]  # Threshold at 0.5
-    confidence = prediction[0][0]
+# 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]
 
-    return predicted_class, confidence
+    # 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]
 
-# Function to generate a PDF report
-def generate_report(name, age, weight, height, img):
-    # Predict result
-    predicted_class, confidence = predict_image(img)
+    # 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)
 
-    # Create PDF
-    pdf = FPDF()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    pdf.add_page()
-    pdf.set_font("Arial", size=12)
+    # 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()
 
-    # Add title
-    pdf.set_font("Arial", style='B', size=16)
-    pdf.cell(200, 10, "Bone Fracture Detection Report", ln=True, align='C')
-    pdf.ln(10)
-
-    # Add patient details
-    pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, f"Patient Name: {name}", ln=True)
-    pdf.cell(200, 10, f"Age: {age}", ln=True)
-    pdf.cell(200, 10, f"Weight: {weight} kg", ln=True)
-    pdf.cell(200, 10, f"Height: {height} cm", ln=True)
-    pdf.cell(200, 10, f"Diagnosis Date: {datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')}", ln=True)
-    pdf.ln(10)
-
-    # Add prediction result
-    pdf.set_font("Arial", style='B', size=14)
-    pdf.cell(200, 10, f"Diagnosis: {predicted_class}", ln=True)
-    pdf.set_font("Arial", size=12)
-    pdf.cell(200, 10, f"Confidence: {confidence:.2f}", ln=True)
-
-    # Save PDF
-    pdf_filename = "patient_report.pdf"
-    pdf.output(pdf_filename)
-
-    return pdf_filename
+    return report_path
 
 # Define Gradio Interface
 interface = gr.Interface(
@@ -70,13 +75,13 @@ interface = gr.Interface(
     inputs=[
         gr.Textbox(label="Patient Name"),
         gr.Number(label="Age"),
-        gr.Number(label="Weight (kg)"),
-        gr.Number(label="Height (cm)"),
-        gr.Image(type="pil", label="X-ray Image")
+        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 & Diagnosis",
-    description="Fill in the patient details and upload an X-ray image. The system will analyze the image and generate a PDF report with the diagnosis."
+    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