demo.py

import tensorflow as tf
import sentencepiece as spm
import numpy as np

# Paths to the model, tokenizer, and metadata
tflite_model_path = r"C:\Users\dejan\AppData\Local\Google\Chrome SxS\User Data\optimization_guide_model_store\43\E6DC4029A1E4B4C1\EF94C116CBE73994\model.tflite"
spm_model_path = r"C:\Users\dejan\AppData\Local\Google\Chrome SxS\User Data\optimization_guide_model_store\43\E6DC4029A1E4B4C1\EF94C116CBE73994\sentencepiece.model"

# Load the SentencePiece tokenizer model
sp = spm.SentencePieceProcessor()
sp.load(spm_model_path)

# Function to preprocess text input
def preprocess_text(text, sp):
    # Tokenize and convert to ids
    input_ids = sp.encode(text, out_type=int)
    # Ensure input is the correct shape for the model
    return np.array(input_ids, dtype=np.int32).reshape(1, -1)

# Load the TFLite model
interpreter = tf.lite.Interpreter(model_path=tflite_model_path)
interpreter.allocate_tensors()

# Get input and output details for model inference
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()

# Function to generate embeddings for a given text
def generate_embeddings(text):
    # Preprocess text input
    input_data = preprocess_text(text, sp)
    
    # Adjust input tensor size if necessary
    interpreter.resize_tensor_input(input_details[0]['index'], input_data.shape)
    interpreter.allocate_tensors()

    # Set the input tensor with preprocessed data
    interpreter.set_tensor(input_details[0]['index'], input_data)
    
    # Run inference
    interpreter.invoke()
    
    # Extract the embedding output
    embedding = interpreter.get_tensor(output_details[0]['index'])
    return embedding

# Example usage
text = "Sample passage for embedding generation"
embedding = generate_embeddings(text)
print("Generated Embedding:", embedding)