Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 import tensorflow as tf
 import numpy as np
 import json
-from PIL import Image
+from PIL import Image, ImageOps  # Added ImageOps for inversion
 
 # 1. Load Model and Labels
 model = tf.keras.models.load_model('devanagari_model.keras')
@@ -14,15 +14,26 @@ with open('labels.json', 'r') as f:
 def process_image(image):
     # Convert to grayscale (L)
     image = image.convert('L')
+    
+    # --- CRITICAL FIX START ---
+    # Invert colors: Black text/White bg -> White text/Black bg
+    # This matches the UCI dataset format used in training.
+    image = ImageOps.invert(image)
+    # --- CRITICAL FIX END ---
+    
     # Resize to 32x32 (dataset size)
     image = image.resize((32, 32))
+    
     # Convert to array
     img_array = np.array(image)
+    
     # Normalize to 0-1
     img_array = img_array / 255.0
+    
     # Add batch dimension (1, 32, 32, 1)
     img_array = np.expand_dims(img_array, axis=0)
     img_array = np.expand_dims(img_array, axis=-1)
+    
     return img_array
 
 # 3. Prediction Function
@@ -35,9 +46,14 @@ def predict_character(image):
     
     # Get top prediction
     predicted_class_index = np.argmax(predictions)
+    
+    # JSON keys are strings, so cast index to str
     predicted_label = labels[str(predicted_class_index)]
+    
+    # Convert numpy float to python float for Gradio
     confidence = float(np.max(predictions))
     
+    # Return dictionary for Gradio Label output
     return {predicted_label: confidence}
 
 # 4. Gradio Interface