Upload demo.py

demo.py

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+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)