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import gradio as gr | |
from sklearn.metrics.pairwise import cosine_similarity | |
#from sklearn.metrics.pairwise import euclidean_distances | |
def greet(array_of_vectors): | |
# [[0 1],[0 5]] | |
array_of_vectors = array_of_vectors.replace("[[", "") | |
array_of_vectors = array_of_vectors.replace("]]", "") | |
array_of_vectors = array_of_vectors.replace("[", "") | |
array_of_vectors = array_of_vectors.replace("]", "") | |
cleaned = [] | |
vecs = array_of_vectors.split(",") | |
for vec in vecs: | |
arr = vec.split(" ") | |
int_arr = [] | |
for a in arr: | |
int_arr.append(int(a)) | |
cleaned.append(int_arr) | |
similarity_matrix2 = cosine_similarity([[0,4]], [[0,0], [0,4], [0,19]]) | |
similarity_matrix = cosine_similarity([[0,4]], cleaned) | |
# similarity_matrix = euclidean_distances(cleaned, cleaned) | |
results = '' | |
for matrix in similarity_matrix: | |
results = results + str(matrix) + "\n" | |
results = results + "cleaned:" + str(cleaned) + "\n" | |
results = results + "sim:" + str(similarity_matrix) + "\n" | |
results = results + "sim2:" + str(similarity_matrix2) + "\n" | |
return results | |
iface = gr.Interface(fn=greet, inputs="text", outputs="text") | |
iface.launch() |