app.py

import gradio as gr
import tensorflow as tf
import numpy as np
import nltk
from nltk.corpus import cmudict

# Download required NLTK data
nltk.download('averaged_perceptron_tagger')
nltk.download('cmudict')

# Load your model
model = tf.keras.models.load_model('audio_model.h5')

# Preprocess input text
def preprocess_text(text):
    """
    Process the input text to prepare it for the model.
    This could include tokenization, phoneme extraction, etc.
    """
    d = cmudict.dict()
    words = text.lower().split()
    phonemes = []

    for word in words:
        if word in d:
            phonemes.append(d[word][0])
        else:
            # If word not found in cmudict, use a placeholder or skip
            phonemes.append(['UNKNOWN'])
    
    # Flatten the list of phonemes
    flattened_phonemes = [p for sublist in phonemes for p in sublist]
    # Convert phonemes to numeric format for the model (customize this based on your model's input requirements)
    numeric_input = np.array([hash(p) % 1000 for p in flattened_phonemes])
    
    return numeric_input

# Define function to generate sound
def generate_sfx(text):
    """
    Takes input text, preprocesses it, runs it through the model,
    and generates an SFX sound.
    """
    input_data = preprocess_text(text)
    
    # Add batch dimension
    input_data = np.expand_dims(input_data, axis=0)
    
    # Generate prediction
    prediction = model.predict(input_data)

    # Postprocess the output to generate a sound file or data
    # Customize based on how your model outputs audio (e.g., generating a WAV file)
    
    # For now, let's return the prediction array as a placeholder
    return prediction

# Define the Gradio interface
interface = gr.Interface(
    fn=generate_sfx,
    inputs=gr.Textbox(label="Enter a Word", placeholder="Write a Word To Convert it into SFX Sound"),
    outputs="numpy",  # Assuming the model output is numerical, you can change this to audio or any other type as needed.
    live=False,
    title="SFX Generator from Text",
    description="Enter a word or sentence, and the model will generate an SFX sound.",
)

# Run the interface
if __name__ == "__main__":
    interface.launch()