Create app.py

app.py

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+import os
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"  # Force TensorFlow to use CPU
+
+import gradio as gr
+import numpy as np
+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
+import requests
+import smtplib
+from email.mime.multipart import MIMEMultipart
+from email.mime.text import MIMEText
+from email.mime.base import MIMEBase
+from email import encoders
+import io
+import base64
+
+# FastAPI server URL
+FASTAPI_URL = "http://localhost:7860/analyze"  # Adjust if your FastAPI server is running elsewhere
+
+# Email credentials
+SENDER_EMAIL = "[email protected]"
+SENDER_PASSWORD = "1w3r5y7i9pW$"
+
+# 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"))]
+
+def image_to_base64(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode('utf-8')
+
+# Function to process X-ray and generate a PDF report
+def generate_report(name, age, gender, weight, height, allergies, cause, xray, email):
+    image_size = (224, 224)
+
+    # Send X-ray to FastAPI for analysis
+    try:
+        with open(xray, 'rb') as f:
+            files = {'file': (os.path.basename(xray), f)}
+            response = requests.post(FASTAPI_URL, files=files)
+            response.raise_for_status()  # Raise an exception for bad status codes
+            fastapi_results_html = response.text
+    except requests.exceptions.RequestException as e:
+        return f"Error connecting to FastAPI server: {e}"
+
+    # Extract prediction from FastAPI response (you might need to adjust this based on the exact HTML structure)
+    diagnosed_class = "normal"
+    severity = "Not Available"
+    try:
+        # Simple string matching for now - improve this if the HTML structure is complex
+        if "KnochenWächter" in fastapi_results_html:
+            if "Kein Knochenbruch" in fastapi_results_html:
+                diagnosed_class = "normal"
+            elif "Knochenbruch" in fastapi_results_html or "Auffällig" in fastapi_results_html:
+                diagnosed_class = "Fractured"
+
+        if diagnosed_class == "Fractured":
+            if "score-high" in fastapi_results_html:
+                severity = "Severe"
+            elif "score-medium" in fastapi_results_html:
+                severity = "Moderate"
+            else:
+                severity = "Mild"
+        else:
+            severity = "Mild" # Assuming normal is mild
+    except Exception as e:
+        print(f"Error parsing FastAPI response: {e}")
+
+    # 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()
+
+    # Send email with the report
+    subject = "Bone Fracture Detection Report"
+    body = f"Dear {name},\n\nPlease find attached your bone fracture detection report.\n\nSincerely,\nYour Bone Fracture Detection System"
+
+    msg = MIMEMultipart()
+    msg['From'] = SENDER_EMAIL
+    msg['To'] = email
+    msg['Subject'] = subject
+    msg.attach(MIMEText(body))
+
+    with open(report_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= {os.path.basename(report_path)}")
+    msg.attach(part)
+
+    try:
+        with smtplib.SMTP_SSL('smtp.gmail.com', 465) as server:
+            server.login(SENDER_EMAIL, SENDER_PASSWORD)
+            server.sendmail(SENDER_EMAIL, email, msg.as_string())
+        print(f"Report sent successfully to {email}")
+        return report_path  # Return path for auto-download
+    except Exception as e:
+        return f"Error sending email: {e}"
+
+# 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")
+
+    email = gr.Textbox(label="Patient Email Address")
+    submit_button = gr.Button("Generate Report and Send Email")
+    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, email],
+        outputs=[output_file],
+    )
+
+# Launch the Gradio app
+if __name__ == "__main__":
+    app.launch()