Upload graph-byclip.py

graph-byclip.py

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+#!/bin/env python
+
+""" Work in progress
+Plan:
+   Modded version of graph-embeddings.py
+   Just to see if using different CLIP module changes values significantly
+   (It does not)
+   This requires
+       pip install git+https://github.com/openai/CLIP.git
+"""
+
+
+import sys
+import json
+import torch
+import clip
+
+import PyQt5
+import matplotlib
+matplotlib.use('QT5Agg')  # Set the backend to TkAgg
+
+import matplotlib.pyplot as plt
+
+CLIPname= "ViT-L/14"
+
+device=torch.device("cuda")
+print("loading CLIP model")
+model, processor = clip.load(CLIPname,device=device)
+model.cuda().eval()
+print("done")
+
+def embed_from_text(text):
+    tokens = clip.tokenize(text).to(device)
+
+    with torch.no_grad():
+        embed = model.encode_text(tokens)
+        return embed
+
+
+# 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
+
+
+fig, ax = plt.subplots()
+
+
+text1 = input("First word or prompt: ")
+text2 = input("Second prompt(or leave blank): ")
+
+
+print("generating embeddings for each now")
+emb1 = embed_from_text(text1)
+print("shape of emb1:",emb1.shape)
+
+graph1=emb1[0].tolist()
+ax.plot(graph1, label=text1[:20])
+
+if len(text2) >0:
+    emb2 = embed_from_text(text2)
+    graph2=emb2[0].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()