Update app.py

app.py

@@ -1,2 +1,51 @@
-import gradio as gr
-gr.Interface.load("models/Bajiyo/Malayalam_transliteration").launch()
+from gradio import Interface
+import json
+from tensorflow.keras.models import load_model  # Assuming TensorFlow backend
+from keras.preprocessing.text import Tokenizer  # Assuming Keras Tokenizer
+
+# Model and tokenizer loading paths (replace with your actual paths)
+model_path = "Bajiyo/Malayalam_transliteration"
+source_tokenizer_config_path = "Bajiyo/Malayalam_transliteration"
+target_tokenizer_config_path = "Bajiyo/Malayalam_transliteration"
+
+# Load the model
+model = load_model(model_path)
+
+# Load tokenizers
+with open(source_tokenizer_config_path, "r") as f:
+    source_tokenizer = Tokenizer.from_config(json.load(f))
+
+with open(target_tokenizer_config_path, "r") as f:
+    target_tokenizer = Tokenizer.from_config(json.load(f))
+
+
+def transliterate(malayalam_name):
+    # Preprocess input (e.g., handle punctuation, special characters)
+    processed_name = preprocess_malayalam_name(malayalam_name)  # Implement your preprocessing logic
+
+    # Tokenize the input
+    sequence = source_tokenizer.texts_to_sequences([processed_name])[0]
+
+    # Pad the sequence
+    padded_sequence = pad_sequences([sequence], maxlen=MAX_SEQ_LENGTH, padding="post")
+
+    # Make prediction
+    prediction = model.predict(padded_sequence)[0]
+
+    # Detokenize the predicted sequence
+    transliterated_name = target_tokenizer.sequences_to_texts([np.argmax(prediction)])[0]
+
+    return transliterated_name
+
+# Define the maximum sequence length your model was trained on
+MAX_SEQ_LENGTH = ...  # Replace with the actual value
+
+interface = Interface(
+    fn=transliterate,
+    inputs="text",
+    outputs="text",
+    title="Malayalam to English Transliteration",
+    description="Enter a Malayalam name and get the transliterated English version.",
+)
+
+interface.launch()