Update app.py

app.py

@@ -1,5 +1,31 @@
 import gradio as gr
+from keras.models import load_model
+from keras.preprocessing.sequence import pad_sequences
 
-model = gr.Interface.load("your-username/your-model-name")
+# Load your custom Keras model
+model = load_model('models/Bajiyo/Named_entity_transliteration_malayalam"')
+
+# Function for transliteration
+def transliterate_malayalam_to_english(malayalam_text):
+    # Tokenize and preprocess the input (adjust this based on your preprocessing logic)
+    input_sequence = pad_sequences(tokenizer.texts_to_sequences([malayalam_text]), maxlen=max_seq_length, padding='post')
+    
+    # Use the model for prediction
+    output_sequence = model.predict(input_sequence)
+    
+    # Decode the output sequence to get the transliterated text (adjust this based on your decoding logic)
+    predicted_text = decode_output_sequence(output_sequence)
+    
+    return predicted_text
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=transliterate_malayalam_to_english,
+    inputs=gr.Textbox(prompt="Enter Malayalam Text", lines=5),
+    outputs=gr.Textbox(prompt="Transliterated English Text", lines=5),
+    live=True
+)
+
+# Launch the Gradio interface
+iface.launch()
 
-model.launch()