feat: nitial commit

main.py

@@ -1,52 +1,7 @@
-import numpy as np
-import spacy
-from spacy.lang.pl.examples import sentences 
-
-nlp = spacy.load("pl_core_news_sm")
-vocab = list(nlp.vocab.strings)
-
-def levenshtein(seq1, seq2):
-    size_x = len(seq1) + 1
-    size_y = len(seq2) + 1
-    matrix = np.zeros ((size_x, size_y))
-    for x in range(size_x):
-        matrix [x, 0] = x
-    for y in range(size_y):
-        matrix [0, y] = y
-
-    for x in range(1, size_x):
-        for y in range(1, size_y):
-            if seq1[x-1] == seq2[y-1]:
-                matrix [x,y] = min(
-                    matrix[x-1, y] + 1,
-                    matrix[x-1, y-1],
-                    matrix[x, y-1] + 1
-                )
-            else:
-                matrix [x,y] = min(
-                    matrix[x-1,y] + 1,
-                    matrix[x-1,y-1] + 1,
-                    matrix[x,y-1] + 1
-                )
-    return (matrix[size_x - 1, size_y - 1])
-
-def get_levenshtein_distances(word):
-    global vocab
-    distances = {}
-    for elem in vocab:
-        distance = levenshtein(word, elem)
-        if distance in distances.keys():
-            distances[distance].append(elem)
-        else: distances[distance] = [elem]
-
-    return distances[min(distances.keys())], min(distances.keys())
-
-
-distances = get_levenshtein_distances("glowa")
-
-print(distances)
-
-
-
+import gradio as gr
 
+def greet(name):
+    return "Hello " + name + "!!"
 
+iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+iface.launch()

setup.sh

@@ -1 +1 @@
-poetry run spacy download pl_core_news_sm
+sleep 1