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app.py

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+import streamlit as st
+from nltk.corpus import cmudict
+from difflib import SequenceMatcher
+
+# Load CMU Pronouncing Dictionary
+d = cmudict.dict()
+
+# Function to get phonetic transcription of a word
+def phonetic_transcription(word):
+    try:
+        return d[word.lower()][0]
+    except KeyError:
+        return None
+
+# Function to calculate phonetic similarity between two words
+def phonetic_similarity(word1, word2):
+    pt1 = phonetic_transcription(word1)
+    pt2 = phonetic_transcription(word2)
+    if pt1 is None or pt2 is None:
+        return 0
+    else:
+        return SequenceMatcher(None, pt1, pt2).ratio()
+
+# User input for list of words and similarity threshold
+words = st.text_input("Enter list of words (separated by commas):")
+threshold = st.slider("Similarity threshold:", min_value=0.0, max_value=1.0, value=0.5)
+
+if words:
+    words = [word.strip() for word in words.split(",")]
+    n_words = len(words)
+
+    # Calculate phonetic similarity matrix
+    similarity_matrix = [[0 for _ in range(n_words)] for _ in range(n_words)]
+    for i in range(n_words):
+        for j in range(i+1, n_words):
+            similarity = phonetic_similarity(words[i], words[j])
+            similarity_matrix[i][j] = similarity
+            similarity_matrix[j][i] = similarity
+
+    # Find similar words based on similarity threshold
+    similar_words = []
+    for i in range(n_words):
+        similar_words.append([words[j] for j in range(n_words) if similarity_matrix[i][j] >= threshold])
+
+    # Display similar words with matching score
+    for i in range(n_words):
+        st.write(f"{words[i]}: {[f'{word} ({int(similarity_matrix[i][j]*100)}%)' for j, word in enumerate(similar_words[i])]}")

requirements.txt

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+streamlit
+nltk