Spaces:
Runtime error
Runtime error
File size: 4,494 Bytes
8a39e1b b487388 064ae46 4da4461 d4e3eb1 b487388 d4e3eb1 b487388 d4e3eb1 064ae46 b487388 8a39e1b b487388 8a39e1b 8987e14 8a39e1b b487388 8a39e1b b487388 8a39e1b b487388 8a39e1b 206e3c8 85658e3 206e3c8 8a39e1b 1e51497 2a6b47b |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 |
import os
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics.pairwise import cosine_similarity
from sentence_transformers import SentenceTransformer
import spacy
import gradio as gr
import subprocess
# def download_spacy_model(model_name):
# command = f"python -m spacy download {model_name}"
# process = subprocess.Popen(command, shell=True, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
# stdout, stderr = process.communicate()
# # Check if the command executed successfully
# if process.returncode != 0:
# print(f"An error occurred while downloading the model: {stderr.decode('utf-8')}")
# else:
# print(f"Successfully downloaded the model: {stdout.decode('utf-8')}")
# Call the function to download the model
# def find_closest(query):
# files_contents = []
# files_names = []
# for file in os.listdir():
# if file.endswith(".txt"):
# with open(file, 'r') as f:
# content = f.read()
# files_contents.append(content)
# files_names.append(file)
# # Append query to the end
# files_contents.append(query)
# # Initialize the TfidfVectorizer
# tfidf_vectorizer = TfidfVectorizer()
# # Fit and transform the texts
# tfidf_matrix = tfidf_vectorizer.fit_transform(files_contents)
# # Compute the cosine similarity between the query and all files
# similarity_scores = cosine_similarity(tfidf_matrix[-1:], tfidf_matrix[:-1])
# # Get the index of the file with the highest similarity score
# max_similarity_idx = similarity_scores.argmax()
# # Return the name of the file with the highest similarity score
# return files_names[max_similarity_idx]
# def find_closest(query):
# try:
# nlp = spacy.load('en_core_web_md')
# except:
# download_spacy_model('en_core_web_md')
# nlp = spacy.load('en_core_web_md')
# files_names = []
# files_vectors = []
# for file in os.listdir():
# if file.endswith(".txt"):
# with open(file, 'r') as f:
# content = f.read()
# files_names.append(file)
# # Get the vector representation of the content
# files_vectors.append(nlp(content).vector)
# # Get the vector representation of the query
# query_vector = nlp(query).vector
# # Compute the cosine similarity between the query and all files
# similarity_scores = cosine_similarity([query_vector], files_vectors)
# # Get the index of the file with the highest similarity score
# max_similarity_idx = similarity_scores.argmax()
# # Return the name of the file with the highest similarity score
# return files_names[max_similarity_idx]
def find_closest(query):
model = SentenceTransformer('all-MiniLM-L6-v2') # You can choose other models
files_contents = []
files_names = []
for file in os.listdir():
if file.endswith(".txt"):
with open(file, 'r') as f:
content = f.read()
files_contents.append(content)
files_names.append(file)
# Append query to the end
files_contents.append(query)
# Create sentence embeddings for each text
embeddings = model.encode(files_contents)
# Compute the cosine similarity between the query and all files
similarity_scores = cosine_similarity([embeddings[-1]], embeddings[:-1])
# Get the index of the file with the highest similarity score
max_similarity_idx = similarity_scores.argmax()
# Return the name of the file with the highest similarity score
return files_names[max_similarity_idx]
def find_closest_mp3(query):
closest_txt_file = find_closest(query)
file_name_without_extension, _ = os.path.splitext(closest_txt_file)
return file_name_without_extension + '.mp3'
my_theme = gr.Theme.from_hub("ysharma/llamas")
with gr.Blocks(theme=my_theme) as demo:
gr.Markdown("""<h1 style="text-align: center;">BeatLlama Dreambooth</h1>""")
# video=gr.PlayableVideo("final_video.mp4
gr.Markdown("""<h2 style="text-align: center;"><span style="color: white;"> Get a song for your dream, but sung by AI!</span></h2>""")
inp=gr.Textbox(placeholder="Describe your dream!",label="Your dream")
out=gr.Audio(label="Llamas singing your dream")
inp.change(find_closest_mp3,inp,out,scroll_to_output=True)
out.play(None)
demo.queue(1,api_open=False)
demo.launch(show_api=False) |