Update app.py

app.py

@@ -6,10 +6,30 @@ 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("model.pth")  
+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
@@ -26,7 +46,10 @@ def generate_report(name, age, gender, weight, height, allergies, injury_cause,
     # 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)
@@ -63,7 +86,13 @@ def generate_report(name, age, gender, weight, height, allergies, injury_cause,
         xray.save(xray_path)
         pdf.image(xray_path, x=40, w=130)
         os.remove(xray_path)
-        pdf.ln(10)
+        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)
@@ -98,7 +127,8 @@ def generate_report(name, age, gender, weight, height, allergies, injury_cause,
 # Function to send email with PDF report
 def send_email(email, patient_name, hospital_name, pdf_path):
     sender_email = "[email protected]"
-    sender_password = "your_password"
+    sender_password = "your_app_password"  # Use App Password
+
     subject = f"Patient Report - {patient_name}"
     
     message = MIMEMultipart()
@@ -129,18 +159,8 @@ def send_email(email, patient_name, hospital_name, pdf_path):
 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.")
-    
-    name = gr.Textbox(label="Patient Name", max_length=50)
-    age = gr.Number(label="Age")
-    gender = gr.Dropdown(label="Gender", choices=["Male", "Female", "Other"])
-    weight = gr.Number(label="Weight (kg)")
-    height = gr.Number(label="Height (cm)")
-    allergies = gr.Textbox(label="Allergies", max_length=100)
-    injury_cause = gr.Textbox(label="Cause of Injury", max_length=500)
-    address = gr.Textbox(label="Address", max_length=150)
-    parent_name = gr.Textbox(label="Parent/Guardian Name", max_length=50)
-    email = gr.Textbox(label="Patient Email", type="email")
-    xray = gr.Image(label="Upload X-ray", type="pil")
+
+    xray = gr.Image(label="Upload X-ray", type="pil", value="samples/sample_xray.jpg")
 
     submit = gr.Button("Generate Report")
     output = gr.File()