app.py

from huggingface_hub import from_pretrained_keras
import gradio as gr
import json

# Set the path to the directory containing the model files
model_directory = "Bajiyo/Malayalam_transliteration"

# Set the paths to the tokenizer configuration files
source_tokenizer_config_path = f"{model_directory}/source_tokenizer_config.json"
target_tokenizer_config_path = f"{model_directory}/target_tokenizer_config.json"

# Load tokenizer configurations
with open(source_tokenizer_config_path, "r") as source_config_file:
    source_tokenizer_config = json.load(source_config_file)

with open(target_tokenizer_config_path, "r") as target_config_file:
    target_tokenizer_config = json.load(target_config_file)

# Load the model from Hugging Face
model = from_pretrained_keras("Bajiyo/Malayalam_transliteration")

def transliterate(input_text):
    # Tokenize the input text using the loaded tokenizer configurations
    # (Assuming you have functions to tokenize input_text using source_tokenizer_config and target_tokenizer_config)
    inputs = tokenize_input(input_text, source_tokenizer_config)
    
    # Make predictions using the model
    predictions = model.predict(inputs)
    
    # Post-process the predictions if needed
    output_text = post_process_predictions(predictions, target_tokenizer_config)
    
    return output_text

# Define Gradio interface
inputs = gr.inputs.Textbox(label="Enter Malayalam Text")
outputs = gr.outputs.Textbox(label="Transliteration to English")
interface = gr.Interface(transliterate, inputs, outputs, title="Malayalam to English Transliteration")
interface.launch()