removed scikit learn dependency

gradio.egg-info/requires.txt

@@ -7,7 +7,6 @@ Flask-BasicAuth
 paramiko
 scipy
 IPython
-scikit-learn
 scikit-image
 analytics-python
 pandas

gradio/embeddings.py

@@ -1,8 +1,38 @@
 import numpy as np
-from sklearn.decomposition import PCA
 
 SMALL_CONST = 1e-10
 
+class PCA:
+    """
+    Credit: https://www.python-engineer.com/courses/mlfromscratch/11_pca/
+    """
+    def __init__(self, n_components, random_state):
+        self.n_components = n_components
+        self.components = None
+        self.mean = None
+        self.random_state = random_state
+
+    def fit(self, X):
+        np.random.seed(self.random_state)
+        self.mean = np.mean(X, axis=0)
+        X = X - self.mean
+        cov = np.cov(X.T)
+        eigenvalues, eigenvectors = np.linalg.eig(cov)
+        eigenvectors = eigenvectors.T
+        idxs = np.argsort(eigenvalues)[::-1]
+        eigenvalues = eigenvalues[idxs]
+        eigenvectors = eigenvectors[idxs]
+        self.components = np.real(eigenvectors[0:self.n_components])
+
+    def transform(self, X):
+        X = X - self.mean
+        return np.dot(X, self.components.T)
+
+    def fit_transform(self, X):
+        self.fit(X)
+        return self.transform(X)
+
+
 def calculate_similarity(embedding1, embedding2):
     """
     Scores the similarity between two embeddings by taking the cosine similarity

setup.py

@@ -23,7 +23,6 @@ setup(
         'paramiko',
         'scipy',
         'IPython',
-        'scikit-learn',
         'scikit-image',
         'analytics-python',
         'pandas',