clipmodel-graph-embeddings.py

#!/bin/env python

""" Work in progress
Plan:
   Generate two embeddings, from text prompts.
   Create comparative graph of their values
"""


import sys
import json
import torch
from transformers import CLIPProcessor,CLIPModel

import PyQt5
import matplotlib
matplotlib.use('QT5Agg')  # Set the backend to TkAgg

import matplotlib.pyplot as plt


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)

# Expect SINGLE WORD ONLY
def standard_embed_calc(text):
    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


init()

fig, ax = plt.subplots()


text1 = input("First word or prompt: ")
text2 = input("Second prompt(or leave blank): ")


print("generating embeddings for each now")
emb1 = standard_embed_calc(text1)
graph1=emb1.tolist()
ax.plot(graph1, label=text1[:20])

if len(text2) >0:
    emb2 = standard_embed_calc(text2)
    graph2=emb2.tolist()
    ax.plot(graph2, label=text2[:20])

# Add labels, title, and legend
#ax.set_xlabel('Index')
ax.set_ylabel('Values')
ax.set_title('Comparative Graph of Two Embeddings')
ax.legend()

# Display the graph
print("Pulling up the graph")
plt.show()