!pip install gradio
!apt-get install timidity
!pip install transformers

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

#load poem generation model
model = TFAutoModelForCausalLM.from_pretrained('drive/MyDrive/FIRE_3rd Sem/peom_gn/')
base_model = "distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(base_model)



#load sentiment analysis
model_v1 = TFAutoModelForSequenceClassification.from_pretrained('drive/MyDrive/FIRE_3rd Sem/sen_analysis/bert')
base_model = "distilbert-base-uncased"
tokenizer_v1 = AutoTokenizer.from_pretrained(base_model)

#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 GENERATION
def inference_music_gen():
  return Path(path_wav_file)

music_gen_interface = gr.Interface(
    inference_music_gen, 
    inputs = None, 
    outputs = gr.outputs.Audio(type="filepath", label="Output")
    )

#SENTIMENT ANALYSIS
def inference_sentiment_analysis(sen):
  tokenized_v1 = tokenizer_v1([sen], return_tensors="np", padding="longest")
  outputs_v1 = model_v1(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",
)

#POEM GENERATION
def inference_poem_gen(start):
  tokenized = tokenizer(start, return_tensors="np")
  outputs = model.generate(**tokenized, max_new_tokens=20)
  res = tokenizer.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
title = "Music Generation"
description = "Add Project description"
article = "<p style='text-align: center'><a href='https://github.com/' target='_blank'>Github Repo</a></p>"
#we can add other project related stuff as well

demo = gr.TabbedInterface([music_gen_interface, poem_gen_interface, sentiment_analysis_interface], 
                          ["Music Generation", "Poem Generation", "Sentiment Analysis"])
demo.launch(debug=True, share=True)