app.py

import gradio as gr
import json
import numpy as np
from sentence_transformers import SentenceTransformer

model = SentenceTransformer('all-MiniLM-L6-v2')
model.max_seq_length = 256

class NumpyEncoder(json.JSONEncoder):
    def default(self, obj):
        if isinstance(obj, np.ndarray):
            return obj.tolist()
        return json.JSONEncoder.default(self, obj)
        
def text_to_embedding(text):
    # Tokenize the input text
    tokens = model.tokenize(text)

    # Check if the token count exceeds the model's maximum sequence length
    if len(tokens) > model.max_seq_length:

        # Split the input text into chunks
        chunks = []
        for i in range(0, len(tokens), model.max_seq_length):
            chunk = tokens[i:i + model.max_seq_length]
            chunks.append(model.tokenizer.convert_tokens_to_string(chunk))

        # Encode each chunk and store the embeddings
        embeddings = []
        for chunk in chunks:
            embedding = model.encode(chunk)
            embeddings.append(embedding)

        # Calculate the average embedding
        avg_embedding = np.mean(embeddings, axis=0)

    else:
        # If the token count is within the limit, just encode the input text
        avg_embedding = model.encode(text)

    return json.dumps(avg_embedding, cls=NumpyEncoder)

inputs = gr.inputs.Textbox(default="Type text here.")
outputs = gr.outputs.Textbox()

app = gr.Interface(fn=text_to_embedding, inputs=inputs, outputs=outputs, title="Text to Embedding")
app.launch()