Delete app.py

app.py

@@ -1,170 +0,0 @@
-import os
-import gradio as gr
-from fpdf import FPDF
-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 torch
-from torchvision import transforms
-from PIL import Image
-
-# Set environment variable to disable GPU if needed
-os.environ["CUDA_VISIBLE_DEVICES"] = ""
-
-# Load the trained fracture detection model
-model = torch.load("my_keras_model.h5")  
-model.eval()
-
-# Function to predict fracture
-def predict_fracture(xray):
-    transform = transforms.Compose([
-        transforms.Resize((224, 224)),  
-        transforms.ToTensor(),
-    ])
-    image = transform(xray).unsqueeze(0)
-    with torch.no_grad():
-        output = model(image)
-        predicted_class = "Fractured" if torch.argmax(output) == 1 else "Not Fractured"
-        confidence = torch.nn.functional.softmax(output, dim=1).max().item() * 100
-    return predicted_class, confidence
-
-# Function to generate PDF report
-def generate_report(name, age, gender, weight, height, allergies, injury_cause, address, parent_name, email, xray):
-    # Ensure input limits
-    name = name[:50] if name else "N/A"
-    age = str(age) if age else "N/A"
-    gender = gender if gender else "N/A"
-    weight = str(weight) + " kg" if weight else "N/A"
-    height = str(height) + " cm" if height else "N/A"
-    allergies = allergies[:100] if allergies else "None"
-    injury_cause = injury_cause[:500] if injury_cause else "Not specified"
-    address = address[:150] if address else "N/A"
-    parent_name = parent_name[:50] if parent_name else "N/A"
-    
-    # Fake hospital details
-    hospital_name = "CityCare Orthopedic Hospital"
-    hospital_address = "123 Medical Lane, Health City, Country"
-
-    # Predict fracture
-    prediction, confidence = predict_fracture(xray)
-
-    # Create PDF
-    pdf = FPDF()
-    pdf.set_auto_page_break(auto=True, margin=15)
-    pdf.add_page()
-    
-    # Title
-    pdf.set_font("Arial", style="B", size=14)
-    pdf.cell(200, 10, hospital_name, ln=True, align="C")
-    pdf.set_font("Arial", size=10)
-    pdf.cell(200, 5, hospital_address, ln=True, align="C")
-    pdf.ln(10)
-
-    # Patient Information
-    pdf.set_font("Arial", style="B", size=12)
-    pdf.cell(200, 10, "Patient Report", ln=True, align="C")
-    pdf.ln(5)
-
-    pdf.set_font("Arial", size=10)
-    pdf.cell(200, 5, f"Patient Name: {name}", ln=True)
-    pdf.cell(200, 5, f"Age: {age}  |  Gender: {gender}", ln=True)
-    pdf.cell(200, 5, f"Weight: {weight}  |  Height: {height}", ln=True)
-    pdf.cell(200, 5, f"Allergies: {allergies}", ln=True)
-    pdf.cell(200, 5, f"Cause of Injury: {injury_cause}", ln=True)
-    pdf.cell(200, 5, f"Address: {address}", ln=True)
-    pdf.cell(200, 5, f"Parent/Guardian: {parent_name}", ln=True)
-    pdf.ln(10)
-
-    # X-ray image
-    if xray:
-        pdf.set_font("Arial", style="B", size=12)
-        pdf.cell(200, 10, "X-ray Image", ln=True, align="C")
-        pdf.ln(5)
-        xray_path = "temp_xray.png"
-        xray.save(xray_path)
-        pdf.image(xray_path, x=40, w=130)
-        os.remove(xray_path)
-        pdf.ln(5)
-
-        # Prediction result
-        pdf.set_font("Arial", style="B", size=10)
-        pdf.cell(200, 5, f"Prediction: {prediction} (Confidence: {confidence:.2f}%)", ln=True, align="C")
-
-    pdf.ln(10)
-
-    # Diagnosis and Recommendation
-    pdf.set_font("Arial", style="B", size=12)
-    pdf.cell(200, 10, "Diagnosis & Recommendations", ln=True)
-    pdf.set_font("Arial", size=10)
-    pdf.multi_cell(0, 5, "Based on the provided X-ray and details, the following suggestions are recommended:")
-    
-    pdf.set_font("Arial", style="I", size=10)
-    pdf.cell(200, 5, "- Immediate medical consultation is advised.", ln=True)
-    pdf.cell(200, 5, "- Pain management with prescribed medications.", ln=True)
-    pdf.cell(200, 5, "- Possible surgical intervention if required.", ln=True)
-    pdf.cell(200, 5, "- Rest and immobilization of the affected area.", ln=True)
-    pdf.cell(200, 5, "- Follow-up X-ray and rehabilitation therapy.", ln=True)
-    
-    pdf.ln(5)
-    pdf.set_font("Arial", style="B", size=10)
-    pdf.cell(200, 5, "Estimated Treatment Costs:", ln=True)
-    
-    pdf.set_font("Arial", size=10)
-    pdf.cell(200, 5, "Government Hospital: $500 - $1,200", ln=True)
-    pdf.cell(200, 5, "Private Hospital: $2,000 - $5,000", ln=True)
-
-    # Save PDF
-    pdf_path = "patient_report.pdf"
-    pdf.output(pdf_path)
-
-    # Send email
-    send_email(email, name, hospital_name, pdf_path)
-
-    return pdf_path
-
-# Function to send email with PDF report
-def send_email(email, patient_name, hospital_name, pdf_path):
-    sender_email = "[email protected]"
-    sender_password = "your_app_password"  # Use App Password
-
-    subject = f"Patient Report - {patient_name}"
-    
-    message = MIMEMultipart()
-    message["From"] = sender_email
-    message["To"] = email
-    message["Subject"] = subject
-    body = f"Dear {patient_name},\n\nYour medical report from {hospital_name} is attached. Please review the details and consult a doctor if needed.\n\nBest regards,\n{hospital_name}"
-    message.attach(MIMEText(body, "plain"))
-
-    with open(pdf_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={pdf_path}")
-        message.attach(part)
-
-    try:
-        server = smtplib.SMTP("smtp.gmail.com", 587)
-        server.starttls()
-        server.login(sender_email, sender_password)
-        server.sendmail(sender_email, email, message.as_string())
-        server.quit()
-        print("Email sent successfully!")
-    except Exception as e:
-        print(f"Error sending email: {e}")
-
-# Gradio Interface
-with gr.Blocks() as app:
-    gr.Markdown("# Bone Fracture Detection & Diagnosis")
-    gr.Markdown("Upload an X-ray, enter patient details, and get a report with treatment suggestions.")
-
-    xray = gr.Image(label="Upload X-ray", type="pil", value="samples/sample_xray.jpg")
-
-    submit = gr.Button("Generate Report")
-    output = gr.File()
-
-    submit.click(generate_report, [name, age, gender, weight, height, allergies, injury_cause, address, parent_name, email, xray], output)
-
-app.launch()