Update app.py

app.py

@@ -2,7 +2,6 @@ import gradio as gr
 import spaces
 import torch
 from transformers import AutoTokenizer, AutoModel
-from sklearn.decomposition import PCA
 import plotly.graph_objects as go
 from huggingface_hub import HfApi
 from huggingface_hub import hf_hub_download
@@ -11,7 +10,7 @@ import sys
 
 model_name = "sentence-transformers/all-MiniLM-L6-v2"
 tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = None  # We'll load the model inside the GPU-enabled function
+model = None
 
 @spaces.GPU
 def get_embedding(text):
@@ -24,17 +23,17 @@ def get_embedding(text):
         outputs = model(**inputs)
     return outputs.last_hidden_state.mean(dim=1).squeeze().cpu().numpy()
 
-def compress_to_3d(embedding):
-    pca = PCA(n_components=3)
-    return pca.fit_transform(embedding.reshape(1, -1))[0]
+def reduce_to_3d(embedding):
+    # Instead of PCA, we'll just take the first 3 dimensions
+    return embedding[:3]
 
 @spaces.GPU
 def compare_embeddings(text1, text2):
     emb1 = get_embedding(text1)
     emb2 = get_embedding(text2)
     
-    emb1_3d = compress_to_3d(emb1)
-    emb2_3d = compress_to_3d(emb2)
+    emb1_3d = reduce_to_3d(emb1)
+    emb2_3d = reduce_to_3d(emb2)
     
     fig = go.Figure(data=[
         go.Scatter3d(x=[0, emb1_3d[0]], y=[0, emb1_3d[1]], z=[0, emb1_3d[2]], mode='lines+markers', name='Text 1'),