Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+import json
+from PIL import Image
+
+# 1. Load Model and Labels
+model = tf.keras.models.load_model('devanagari_model.keras')
+
+with open('labels.json', 'r') as f:
+    labels = json.load(f)
+
+# 2. Preprocessing Function
+def process_image(image):
+    # Convert to grayscale (L)
+    image = image.convert('L')
+    # 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
+def predict_character(image):
+    if image is None:
+        return "Please upload an image."
+    
+    processed_img = process_image(image)
+    predictions = model.predict(processed_img)
+    
+    # Get top prediction
+    predicted_class_index = np.argmax(predictions)
+    predicted_label = labels[str(predicted_class_index)]
+    confidence = float(np.max(predictions))
+    
+    return {predicted_label: confidence}
+
+# 4. Gradio Interface
+iface = gr.Interface(
+    fn=predict_character,
+    inputs=gr.Image(type="pil", label="Upload Character Image"),
+    outputs=gr.Label(num_top_classes=3),
+    title="Devanagari Character Recognition (Lightweight)",
+    description="Upload a handwritten Hindi/Devanagari character. This model is optimized for low-resource environments."
+)
+
+iface.launch()