Fix file paths

app.py

@@ -1,14 +1,15 @@
 import os
-from flask import Flask, render_template, request, jsonify
+from flask import Flask, render_template, request, jsonify, send_from_directory
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
 import numpy as np
 from PIL import Image
 import io
 
-app = Flask(__name__)
+# Initialize Flask with standard folders
+app = Flask(__name__, template_folder='templates', static_folder='static')
 
-# 1. Load Model (Load once at startup)
+# 1. Load Model
 MODEL_PATH = 'model.h5'
 try:
     model = load_model(MODEL_PATH)
@@ -16,17 +17,11 @@ try:
 except Exception as e:
     print(f"Error loading model: {e}")
 
-# Class names matching your Classification Report
 CLASS_NAMES = ['NonDemented', 'VeryMildDemented', 'MildDemented', 'ModerateDemented']
 
 def prepare_image(img_bytes):
-    # Convert bytes to PIL Image
     img = Image.open(io.BytesIO(img_bytes))
-    
-    # Resize to 224x224 (As we found earlier)
     img = img.resize((224, 224))
-    
-    # Convert to array and normalize
     img_array = image.img_to_array(img)
     img_array = np.expand_dims(img_array, axis=0)
     img_array = img_array / 255.0
@@ -34,12 +29,25 @@ def prepare_image(img_bytes):
 
 # --- ROUTES ---
 
-# 1. Serve the Website
+# 1. Main Page
 @app.route('/')
 def home():
     return render_template('index.html')
 
-# 2. Handle Predictions
+# 2. FIX: Explicitly serve the assets folder
+@app.route('/assets/<path:filename>')
+def serve_assets(filename):
+    return send_from_directory('static/assets', filename)
+
+# 3. FIX: Serve root files (like brain.svg)
+@app.route('/<path:filename>')
+def serve_root_files(filename):
+    # Only serve if the file exists in static (avoids crashing on bad links)
+    if os.path.exists(os.path.join('static', filename)):
+        return send_from_directory('static', filename)
+    return "File not found", 404
+
+# 4. Handle Predictions
 @app.route('/predict', methods=['POST'])
 def predict():
     if 'file' not in request.files:
@@ -48,10 +56,7 @@ def predict():
     file = request.files['file']
     
     try:
-        # Process image
         processed_img = prepare_image(file.read())
-        
-        # Predict
         prediction = model.predict(processed_img)
         class_index = np.argmax(prediction)
         confidence = float(np.max(prediction))