Create UI.ipynb

UI.ipynb

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+!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)