Update main.py

main.py

@@ -88,20 +88,45 @@ def resultbt():
 
             flash('Image successfully uploaded and displayed below')
 
+        
             try:
                 # Process the image
                 img = cv2.imread(temp_file.name)
                 img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)  # Convert to RGB
                 img = crop_imgs([img])
-                img = img.reshape(img.shape[1:])
-                img = preprocess_imgs([img], (128, 128))  # Match model's input size
-                img = np.expand_dims(img, axis=0)  # Add batch dimension
-
+                img = img.reshape(img.shape[1:])  # Reshape to (height, width, channels)
+                img = preprocess_imgs([img], (128, 128))  # Resize to (128, 128, 3)
+                
+                # Ensure the input shape matches the model's expectation
+                img = img[0]  # Remove unnecessary extra dimension
+                img = np.expand_dims(img, axis=0)  # Add batch dimension to match (1, 128, 128, 3)
+            
                 # Make prediction
                 pred = braintumor_model.predict(img)
                 prediction = 'Tumor Detected' if pred[0][0] >= 0.5 else 'No Tumor Detected'
                 confidence_score = float(pred[0][0])
 
+    # Prepare data for MongoDB
+    result = {
+        "firstname": firstname,
+        "lastname": lastname,
+        "email": email,
+        "phone": phone,
+        "gender": gender,
+        "age": age,
+        "image_name": filename,
+        "prediction": prediction,
+        "confidence_score": confidence_score,
+        "timestamp": datetime.utcnow()
+    }
+
+    # Insert data into MongoDB
+    collection.insert_one(result)
+
+    # Return the result to the user
+    return render_template('resultbt.html', filename=filename, fn=firstname, ln=lastname, age=age, r=prediction, gender=gender)
+
+
                 # Prepare data for MongoDB
                 result = {
                     "firstname": firstname,