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"""## Imports"""
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.compat.v1.Session(config=config)
import gradio as gr
from pathlib import Path
import subprocess
from transformers import AutoTokenizer
from transformers import TFAutoModelForCausalLM
from transformers import TFAutoModelForSequenceClassification, AutoTokenizer
import numpy as np
from musicautobot.music_transformer.transform import idxenc2stream, midi2idxenc
from musicautobot.vocab import MusicVocab
import keras
"""## Model Loads"""
#load poem generation model
model_poem_gn = TFAutoModelForCausalLM.from_pretrained('merged-ui/models-misc/peom_gn')
base_model_poem_gn = "distilgpt2"
tokenizer_poem_gn = AutoTokenizer.from_pretrained(base_model_poem_gn)
#load sentiment analysis
model_sa = TFAutoModelForSequenceClassification.from_pretrained('merged-ui/models-misc/sen_analysis/bert')
base_model_sa = "distilbert-base-uncased"
tokenizer_sa = AutoTokenizer.from_pretrained(base_model_sa)
#music generation
"""
base_path = "/content/drive/MyDrive/FIRE_3rd Sem/music_gn/"
#path_mid_file -> Replace this with model generated file path
path_mid_file = base_path + "Comic_Relief.mid"
path_wav_file = base_path + "output_comic.wav"
subprocess.call(['timidity', path_mid_file, "-Ow", "-o", path_wav_file])"""
music_gen_base_path = "merged-ui/music_gen/"
model_music_gen = keras.models.load_model("transformer-final")
"""## Music Generation"""
def predict_music(model, input_vector, num):
normalized = input_vector / 311
for i in range(num):
predict = model.predict(np.reshape(normalized[-100:], (1,100)), verbose = 0)
normalized = np.append(normalized, predict)
result = np.rint(normalized * 311)
# edits to prediction
for i in range(100, len(result)):
if i % 2 == 0:
if abs(result[i] - 8) < 5 and result[i] != 8:
result[i] = 8
else:
if result[i] < 137:
result[i] = 137
return result
# this function takes a 100 length encoded song beginning as an input and
def midi_predict(model, test, num_notes):
test_midi = idxenc2stream(test.astype("int"), MusicVocab.create())
test_midi.write('midi',music_gen_base_path+"input_demo.mid")
res = predict_music(model, test, num_notes)
output = idxenc2stream(res.astype("int"), MusicVocab.create())
output.write('midi',music_gen_base_path+"output_demo.mid")
path_mid_file = music_gen_base_path + "output_demo.mid"
path_wav_file = music_gen_base_path + "output_demo.wav"
# need timidity for this
subprocess.call(['timidity', path_mid_file, "-Ow", "-o", path_wav_file])
return
def inference_music_gen(audio, num_notes):
data_e = midi2idxenc(audio.name, MusicVocab.create())
midi_predict(model_music_gen, data_e[:100], int(num_notes))
return
music_gen_interface = gr.Interface(
inference_music_gen,
inputs = [gr.inputs.File(type="file", label="Input"), gr.Textbox(lines = 1, placeholder = "Enter number of notes here")],
examples=[[music_gen_base_path + "mid_file/Comic_Relief.mid", 300]],
outputs = gr.outputs.Audio(type="filepath", label="Output")
)
"""## Sentiment Analysis"""
def inference_sentiment_analysis(sen):
tokenized_v1 = tokenizer_sa([sen], return_tensors="np", padding="longest")
outputs_v1 = model_sa(tokenized_v1).logits
classifications_v1 = np.argmax(outputs_v1, axis=1)
if classifications_v1[0] == 1:
res = "Positive :)"
else:
res = "Negative :("
return res
sentiment_analysis_interface = gr.Interface(
fn=inference_sentiment_analysis,
inputs=gr.Textbox(lines=2, placeholder="Enter a Sentence"),
outputs="text",
)
"""## Peom Generation"""
def inference_poem_gen(start):
tokenized = tokenizer_poem_gn(start, return_tensors="np")
outputs = model_poem_gn.generate(**tokenized, max_new_tokens=20)
res = tokenizer_poem_gn.decode(outputs[0])
return res.replace("<LINE>", "\n")
poem_gen_interface = gr.Interface(
fn=inference_poem_gen,
inputs=gr.Textbox(lines=2, placeholder="Start Here..."),
outputs="text",
)
"""## Combine All"""
demo = gr.TabbedInterface([music_gen_interface, poem_gen_interface, sentiment_analysis_interface],
["Music Generation", "Poem Generation", "Sentiment Analysis"])
demo.launch(debug=True, share=True)
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