Delete demo.py

demo.py

@@ -1,50 +0,0 @@
-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)