subtracted-distances.py

#!/bin/env python

""" Work in progress
Plan:
   Kinda a "temp" hack (maybe)
   Prompt for TWO things. Subtract the second embed from the first.
   Then see what is near
"""


import sys
import json
import torch
from safetensors import safe_open

from transformers import CLIPProcessor,CLIPModel

clipsrc="openai/clip-vit-large-patch14"
processor=None
model=None

device=torch.device("cuda")


def init():
    global processor
    global model
    # Load the processor and model
    print("loading processor from "+clipsrc,file=sys.stderr)
    processor = CLIPProcessor.from_pretrained(clipsrc)
    print("done",file=sys.stderr)
    print("loading model from "+clipsrc,file=sys.stderr)
    model = CLIPModel.from_pretrained(clipsrc)
    print("done",file=sys.stderr)

    model = model.to(device)



embed_file="embeddings.safetensors"

device=torch.device("cuda")

print("reading words from dictionary now",file=sys.stderr)
with open("dictionary","r") as f:
    tokendict = f.readlines()
    wordlist = [token.strip() for token in tokendict]  # Remove trailing newlines
print(len(wordlist),"lines read")


print("reading embeddings now",file=sys.stderr)
model = safe_open(embed_file,framework="pt",device="cuda")
embs=model.get_tensor("embeddings")
embs.to(device)
print("Shape of loaded embeds =",embs.shape)

def standard_embed_calc(text):
    if processor == None:
        init()

    inputs = processor(text=text, return_tensors="pt")
    inputs.to(device)
    with torch.no_grad():
        text_features = model.get_text_features(**inputs)
    embedding = text_features[0]
    return embedding


def print_distances(targetemb):
    targetdistances = torch.cdist( targetemb.unsqueeze(0), embs, p=2)

    print("shape of distances...",targetdistances.shape)

    smallest_distances, smallest_indices = torch.topk(targetdistances[0], 20, largest=False)

    smallest_distances=smallest_distances.tolist()
    smallest_indices=smallest_indices.tolist()
    for d,i in zip(smallest_distances,smallest_indices):
        print(wordlist[i],"(",d,")")




text1=input("First text? ")
text2=input("Second text? ")
emb1=standard_embed_calc(text1)
emb2=standard_embed_calc(text2)

result=torch.sub(emb1,emb2)
print_distances(result)