Upload app.py

app.py

@@ -84,7 +84,7 @@ Using the same 4 million post dataset, we calculate the conditional probability
 This is done by creating a co-occurrence matrix from our dataset, which records how frequently each pair of tags appears together across all documents. 
 By considering the context in which tags are used, we can now not only correct misspellings and rephrasings but also make more contextually relevant suggestions.
 The "similarity weight" slider controls how much weight these conditional probabilities are given vs how much weight the FastText similarity model is given when suggesting replacements for invalid tags.
-A similarity weight slider value of 0 means that only the FastText model's predictions will be used to calculate similarity scores, and a value of 1 means only the conditioanl probabilities are used (although the FastText model is still used to trim the list of candidates).
+A similarity weight slider value of 0 means that only the FastText model's predictions will be used to calculate similarity scores, and a value of 1 means only the conditional probabilities are used (although the FastText model is still used to trim the list of candidates).
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