app.py

import os
import gradio as gr
import numpy as np
from transformers import AutoTokenizer, AutoModel
# :white_check_mark: Setup environment
os.makedirs(os.environ.get("HF_HOME", "./hf_cache"), exist_ok=True)
hf_token = os.environ.get("HF_TOKEN")
if not hf_token:
    raise EnvironmentError(":x: Environment variable HF_TOKEN is not set.")
# :white_check_mark: Load model and tokenizer
text_tokenizer = AutoTokenizer.from_pretrained(
    "nomic-ai/nomic-embed-text-v1.5",
    trust_remote_code=True,
    token=hf_token,
    cache_dir=os.environ["HF_HOME"]
)
text_model = AutoModel.from_pretrained(
    "nomic-ai/nomic-embed-text-v1.5",
    trust_remote_code=True,
    token=hf_token,
    cache_dir=os.environ["HF_HOME"]
)
# :white_check_mark: Embedding function
def get_text_embeddings(text):
    """
    Converts input text into a dense embedding using the Nomic embedding model.
    These embeddings are used to query Qdrant for semantically relevant document chunks.
    """
    inputs = text_tokenizer(text, return_tensors="pt", padding=True, truncation=True)
    outputs = text_model(**inputs)
    embeddings = outputs.last_hidden_state.mean(dim=1)
    return embeddings[0].detach().numpy()
# :white_check_mark: Gradio interface function
def embed_text_interface(text):
    embedding = get_text_embeddings(text)
    return str(embedding)
# :white_check_mark: Gradio UI
interface = gr.Interface(
    fn=embed_text_interface,
    inputs=gr.Textbox(label="Enter text to embed", lines=5),
    outputs=gr.Textbox(label="Embedding vector"),
    title="Text Embedding with Nomic AI",
    description="Enter some text, and get its embedding vector using Nomic's embedding model."
)
# :white_check_mark: Launch the app
if __name__ == "__main__":
    interface.launch()