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README.md

@@ -3,8 +3,10 @@
 This directory contains utilities for the purpose of browsing the
 "token space" of CLIP ViT-L/14
 
-Primary tool is "generate-distances.py", 
-which allows command-line browsing of words and their neighbours
+Primary tools are:
+
+*  "generate-distances.py": allows command-line browsing of words and their neighbours
+*  "graph-embeddings.py": plots graph of full values of two embeddings
 
 
 ## generate-distances.py
@@ -16,6 +18,11 @@ To run this requires the files "embeddings.safetensors" and "dictionary"
 
 You will need to rename or copy appropriate files for this as mentioned below
 
+## graph-embeddings.py
+
+Run the script. It will ask you for two text strings. 
+Once you enter both, it will plot the graph and display it for you
+
 ### embeddings.safetensors
 
 You can either copy one of the provided files, or generate your own.

graph-embeddings.py

@@ -0,0 +1,79 @@
+#!/usr/bin/python3
+
+""" 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()
+
+text1 = input("First word or prompt? ")
+text2 = input("Second word or prompt? ")
+
+
+print("generating embeddings for each now")
+emb1 = standard_embed_calc(text1)
+emb2 = standard_embed_calc(text2)
+
+graph1=emb1.tolist()
+graph2=emb2.tolist()
+
+fig, ax = plt.subplots()
+
+# Plot the two lists on the same graph using the read labels
+ax.plot(graph1, label=text1[:20])
+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()

requirements.txt

@@ -1,3 +1,5 @@
 torch
 safetensors
 transformers
+PyQt5
+matplotlib