Update main.py

main.py

@@ -5,6 +5,7 @@ import imutils
 import numpy as np
 from tensorflow.keras.models import load_model
 from werkzeug.utils import secure_filename
+import tempfile
 
 # Load the Brain Tumor CNN Model
 braintumor_model = load_model('models/braintumor.h5')
@@ -70,40 +71,40 @@ def brain_tumor():
 @app.route('/resultbt', methods=['POST'])
 def resultbt():
     if request.method == 'POST':
+        # Get user input from the form
+        firstname = request.form['firstname']
+        lastname = request.form['lastname']
+        email = request.form['email']
+        phone = request.form['phone']
+        gender = request.form['gender']
+        age = request.form['age']
         file = request.files['file']
+
         if file and allowed_file(file.filename):
+            # Use tempfile to create a temporary file path
+            temp_file = tempfile.NamedTemporaryFile(delete=False)
             filename = secure_filename(file.filename)
-            filepath = os.path.join(app.config['UPLOAD_FOLDER'], filename)
-
-            # Ensure upload folder exists
-            if not os.path.exists(app.config['UPLOAD_FOLDER']):
-                os.makedirs(app.config['UPLOAD_FOLDER'])
+            file.save(temp_file.name)  # Save file to the temp location
 
-            file.save(filepath)
             flash('Image successfully uploaded and displayed below')
 
-            # Load and preprocess image
-            try:
-                img = cv2.imread(filepath)
-                if img is None:
-                    raise ValueError("Image file is invalid or cannot be read.")
+            # Read and process the image
+            img = cv2.imread(temp_file.name)
+            img = crop_imgs([img])
+            img = img.reshape(img.shape[1:])
+            img = preprocess_imgs([img], (224, 224))
 
-                img = crop_imgs([img])
-                img = img.reshape(img.shape[1:])
-                img = preprocess_imgs([img], (224, 224))
-                img = img.reshape(1, 224, 224, 3)
+            # Model prediction
+            pred = braintumor_model.predict(img)
+            if pred < 0.5:
+                pred = 0
+            else:
+                pred = 1
 
-                pred = braintumor_model.predict(img)
-                pred = 1 if pred >= 0.5 else 0
-
-                return render_template('resultbt.html', filename=filename, r=pred)
-            except Exception as e:
-                flash(f"Error processing image: {str(e)}")
-                return redirect(request.url)
+            return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=pred, gender=gender)
         else:
             flash('Allowed image types are - png, jpg, jpeg')
             return redirect(request.url)
 
-
 if __name__ == '__main__':
     app.run(debug=True)