Updated with initial logic

sgg_app.py

@@ -1,65 +1,61 @@
 import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from transformers import TrainingArguments, Trainer
 
-# def game(difficulty, generate_song, artist_choice, submit_answer):
-#     if generate_song:
-#         # Generate the song and the options based on the difficulty
-#         # In the actual implementation, you would generate the song text and the options based on the difficulty
-#         if difficulty == "Demo":
-#             song_text = "Generated song text for Demo"
-#             options = ["Artist 1", "Artist 2", "Artist 3", "Artist 4"]
-#         elif difficulty == "Medium":
-#             song_text = "Generated song text for Medium"
-#             options = ["Artist 5", "Artist 6", "Artist 7", "Artist 8"]
-#         else:  # Hard
-#             song_text = "Generated song text for Hard"
-#             options = ["Artist 9", "Artist 10", "Artist 11", "Artist 12"]
-#         return {"Generated Song": song_text, "Options": options}
-#     elif submit_answer:
-#         # Check the selected artist and return whether it's correct
-#         correct_answer = "Artist 1"  # Placeholder
-#         return {"Correct Answer": correct_answer == artist_choice}
+def generate_song(state, language_model, generate_song):
 
-# game_interface = gr.Interface(
-#     fn=game, 
-#     inputs=[
-#         gr.Radio(["Demo", "Medium", "Hard"], label="Difficulty", 
-#                      info="The higher the difficulty makes it so that the options for the artists are more similar to one another?"),
-#         gr.Button("Generate Song"),
-#         gr.Radio(["A", "B", "C", "D"], label="Multi-Choice Options", 
-#                  info="Select the artist that you suspect is the correct artist for the song."),
-#         gr.Button("Submit Answer"),
-#                 ], 
-        
-#     outputs=[
-#         gr.Textbox(label="Generated Song"), 
-#         gr.Textbox(label="Options"),
-#         gr.Textbox(label="Correct Answer"),
-#             ], 
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-#     title="Song Generator Guessing Game",
-# )
+    # Logic that takes put the selected language model and generates a song
+    if generate_song:
+        if not language_model:
+            song_text = "Please select a language model before generating a song."
+            return state, song_text, "", ""
+        # Generate the song and the options based on the language_model
+        if language_model == "Custom Gpt2":
+            model_name = "SpartanCinder/GPT2-pretrained-lyric-generation"
+        elif language_model == "Gpt2-Medium":
+            model_name = "gpt2-medium"
+        elif language_model == "facebook/bart-base":
+            model_name = "facebook/bart-base"
+        elif language_model == "Gpt-Neo":
+            model_name = "EleutherAI/gpt-neo-1.3B"
+        else:  # Customized Models
+            model_name = "customized-models"
 
-# game_interface.launch()
+    #tokenzer and text generation logic
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    model = AutoModelForCausalLM.from_pretrained(model_name)
+    input_text = pick_artist()
+    max_length = 128
+    input_ids = tokenizer.encode(input_text, return_tensors="pt")
+    input_ids = input_ids.to(device)
 
-def generate_song(state, difficulty, generate_song):
-    if generate_song:
-        if not difficulty:
-            song_text = "Please select a difficulty level before generating a song."
-            return state, song_text, ""
-        # Generate the song and the options based on the difficulty
-        if difficulty == "Demo":
-            song_text = "Generated song text for Demo"
-            options = ["Artist 1", "Artist 2", "Artist 3", "Artist 4"]
-        elif difficulty == "Medium":
-            song_text = "Generated song text for Medium"
-            options = ["Artist 5", "Artist 6", "Artist 7", "Artist 8"]
-        else:  # Hard
-            song_text = "Generated song text for Hard"
-            options = ["Artist 9", "Artist 10", "Artist 11", "Artist 12"]
-        state['options'] = options
-        state['timer_finished'] = False
-        timer_script = "<div id='progress-bar' style='width: 100%; background-color: #f3f3f3; border: 1px solid #bbb;'><div id='progress' style='height: 20px; width: 0%; background-color: #007bff;'></div></div><script>function startTimer() {var time = 30; var timer = setInterval(function() {time--; document.getElementById('progress').style.width = (time / 30 * 100) + '%'; if (time <= 0) {clearInterval(timer);}}, 1000);}</script>"
-        return state, song_text, ', '.join(options), timer_script
+    if language_model != "customized-models":
+        ### Using Beam search to generate text###
+        # encoded data
+        output = model.generate(input_ids, max_length=max_length, num_beams=5, num_return_sequences=5, do_sample=False, no_repeat_ngram_size=2) # Generate text
+        # Decode output
+        print(tokenizer.decode(output[0], skip_special_tokens=True))
+        # But this output is repeating, so I need ot adjust this so that it is not repeating.
+    else:
+        ### Nucleas Sampling to generate text###
+        # Set the do_sample parameter to True because we are using nucleus sampling  is a probabilistic sampling method
+        # top_p is the probability threshold for nucleus sampling
+        # So, we set top_p to 0.9, which means that the model will sample from the top 90% of the probability distribution
+        # This will help to generate more diverse text that is less repetitive
+        encoded_output = model.generate(input_ids, max_length=max_length, num_return_sequences=5, do_sample=True, top_p = 0.9, ) 
+
+    song_text = tokenizer.decode(encoded_output[0], skip_special_tokens=True)
+
+    # Generate the multiple-choice options
+    options = ["Artist 1", "Artist 2", "Artist 3", "Artist 4"]
+
+    state['options'] = options
+    state['timer_finished'] = False
+    timer_script = "<div id='progress-bar' style='width: 100%; background-color: #f3f3f3; border: 1px solid #bbb;'><div id='progress' style='height: 20px; width: 0%; background-color: #007bff;'></div></div><script>function startTimer() {var time = 30; var timer = setInterval(function() {time--; document.getElementById('progress').style.width = (time / 30 * 100) + '%'; if (time <= 0) {clearInterval(timer);}}, 1000);}</script>"
+    return state, song_text, ', '.join(options), timer_script
 
 def submit_answer(state, artist_choice, submit_answer):
     if submit_answer:
@@ -69,10 +65,20 @@ def submit_answer(state, artist_choice, submit_answer):
         # Check the selected artist and return whether it's correct
         correct_answer = state['options'][0]  # Placeholder
         return {"Correct Answer": correct_answer == artist_choice}
+    
+def pick_artist():
+
+    return "A song in the style of Taylor Swift:"
+
+def generate_artist_options(correct_artist):
+    # Generate 3 incorrect options
+    options = ["Artist 1", "Artist 2", "Artist 3", "Artist 4"]
+    options.remove(correct_artist)
+    return [correct_artist] + options
 
-with gr.Blocks(title="Song Genorator Guessing Game") as game_interface:
+with gr.Blocks(title="Song Generator Guessing Game") as game_interface:
     state = gr.State({'options': []})
-    difficulty = gr.Radio(["Demo", "Medium", "Hard"], label="Difficulty")
+    language_model = gr.Radio(["Custom Gpt2", "Gpt2-Medium", "facebook/bart-base","Gpt-Neo", "Customized Models"], label="Difficulty")
     generate_song_button = gr.Button("Generate Song")
     artist_choice_display = gr.Textbox(interactive=False, label="Multiple-Choice Options")
     artist_choice = gr.Radio(["A", "B", "C", "D"], label="Updated Options", info="Select the artist that you suspect is the correct artist for the song.")
@@ -83,7 +89,7 @@ with gr.Blocks(title="Song Genorator Guessing Game") as game_interface:
 
     generate_song_button.click(
         generate_song,
-        [state, difficulty, generate_song_button],
+        [state, language_model, generate_song_button],
         [state, generated_song, artist_choice_display, timer]
     )
     submit_answer_button.click(