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app.py

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+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Force TensorFlow to use CPU
+
+import gradio as gr
+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
+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")
+
+# 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 process X-ray and generate a PDF report
+def generate_report(name, age, gender, weight, height, allergies, cause, xray):
+    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 table
+    treatment_data = [
+        ["Severity Level", "Recommended Treatment", "Recovery Duration"],
+        ["Mild", "Rest, pain relievers, and 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"]
+    ]
+
+    # Estimated cost & duration table
+    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 image for report
+    img = Image.open(xray).resize((300, 300))
+    img_path = f"{name}_xray.png"
+    img.save(img_path)
+
+    # 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.drawString(200, 770, "Bone Fracture Detection Report")
+
+    # Patient details table
+    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]
+    ]
+
+    # Format and align tables
+    def format_table(data):
+        table = Table(data, colWidths=[270, 270])  # Set 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)
+
+    # Load and insert X-ray image
+    c.drawInlineImage(img_path, 50, 320, width=250, height=250)
+    c.setFont("Helvetica-Bold", 12)
+    c.drawString(120, 290, f"Fractured: {'Yes' if diagnosed_class == 'Fractured' else 'No'}")
+
+    # Draw treatment and 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)
+
+    c.save()
+
+    return report_path  # Return path for auto-download
+
+# Function to select a sample image
+def use_sample_image(sample_image_path):
+    return sample_image_path  # Returns selected sample image filepath
+
+# Define Gradio Interface
+with gr.Blocks() as app:
+    gr.HTML(html_content)  # Display `re.html` content in Gradio
+    gr.Markdown("## Bone Fracture Detection System")
+    
+    with gr.Row():
+        name = gr.Textbox(label="Patient Name")
+        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")
+
+    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")
+
+    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],
+        outputs=[output_file],
+    )
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    app.launch()

requirements.txt

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+flask
+numpy
+opencv-python-headless
+fpdf
+torch
+torchvision
+gradio
+tensorflow
+Pillow
+reportlab