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app.py

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+import os
+
+import numpy as np
+import gradio as gr
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.metrics.pairwise import cosine_similarity
+
+shanty=os.environ.get('SHANTY')
+def compute_cosine_similarity(text1, text2):
+    # Initialize the TfidfVectorizer
+    tfidf_vectorizer = TfidfVectorizer()
+
+    # Fit and transform the texts
+    tfidf_matrix = tfidf_vectorizer.fit_transform([text1, text2])
+
+    # Compute the cosine similarity
+    similarity_score = cosine_similarity(tfidf_matrix[0:1], tfidf_matrix[1:2])
+
+    return similarity_score[0][0]
+
+def text_similarity(text):
+    score= compute_cosine_similarity(shanty,text)
+    return score
+
+with gr.Blocks() as demo:
+  gr.Markdown("# Guess the lyrics of the sea shanty! \n ## Each two seconds of video represents a line")
+  video=gr.PlayableVideo("final_video.mp4")
+  inp=gr.Textbox(placeholder="Enter lyrics of sea shanty!")
+
+  out=gr.Textbox()
+  inp.change(text_similarity,inp,out)
+demo.launch(show_api=False,share=True)

requirements.txt

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+numpy
+sklearn