Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import cv2
+from tensorflow.keras.preprocessing import image
+from PIL import Image, ImageDraw
+from reportlab.lib.pagesizes import letter
+from reportlab.pdfgen import canvas
+import os
+
+# Load the trained model
+model = tf.keras.models.load_model("my_keras_model.h5")
+
+# Define image size
+image_size = (224, 224)
+
+# Function to detect fracture and get bounding box
+def detect_fracture(xray):
+    img = Image.open(xray).convert("RGB").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]
+    
+    # Dummy bounding box for now (assuming model enhancement needed)
+    # Ideally, the model should return bounding box coordinates
+    bbox = (50, 50, 150, 150) if prediction > 0.5 else None  # Placeholder
+    
+    return prediction, bbox, img
+
+# 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
+
+# Function to generate report
+def generate_report(name, age, gender, xray1, xray2):
+    # Analyze X-ray images
+    prediction1, bbox1, img1 = detect_fracture(xray1)
+    prediction2, bbox2, img2 = detect_fracture(xray2)
+
+    avg_prediction = (prediction1 + prediction2) / 2
+    diagnosed_class = "Fractured" if avg_prediction > 0.5 else "Normal"
+    severity, treatment, gov_cost, private_cost = analyze_injury(avg_prediction)
+
+    # Draw bounding box if fracture is detected
+    if bbox1:
+        draw = ImageDraw.Draw(img1)
+        draw.rectangle(bbox1, outline="red", width=5)
+
+    if bbox2:
+        draw = ImageDraw.Draw(img2)
+        draw.rectangle(bbox2, outline="red", width=5)
+
+    # Save modified images
+    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", 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.save()
+
+    return report_path, img1_path, img2_path, diagnosed_class, severity, treatment, gov_cost, private_cost
+
+# Define Gradio interface
+interface = gr.Interface(
+    fn=generate_report,
+    inputs=[
+        gr.Textbox(label="Patient Name"),
+        gr.Number(label="Age"),
+        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"),
+        gr.Image(label="X-ray 1 with Fracture Highlight"),
+        gr.Image(label="X-ray 2 with Fracture Highlight"),
+        gr.Textbox(label="Fracture Detected"),
+        gr.Textbox(label="Injury Severity"),
+        gr.Textbox(label="Recommended Treatment"),
+        gr.Textbox(label="Estimated Cost (Govt Hospital)"),
+        gr.Textbox(label="Estimated Cost (Private Hospital)")
+    ],
+    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 app
+if __name__ == "__main__":
+    interface.launch()