Update app.py

app.py

@@ -9,7 +9,7 @@ from sklearn.model_selection import train_test_split
 from sklearn.preprocessing import OneHotEncoder
 from sklearn.neural_network import MLPClassifier
 from deap import base, creator, tools, algorithms
-from transformers import BloomForCausalLM, BloomTokenizerFast
+from transformers import GPTNeoXForCausalLM, GPTNeoXTokenizerFast
 import torch
 
 # Initialize Example Emotions Dataset
@@ -221,9 +221,9 @@ def handle_idle_state():
 
 # S.O.U.L. (Self-Organizing Universal Learning) Function
 class SOUL:
-    def __init__(self, model_name='bigscience/bloom-1b1'):
-        self.tokenizer = BloomTokenizerFast.from_pretrained(model_name)
-        self.model = BloomForCausalLM.from_pretrained(model_name)
+    def __init__(self, model_name='EleutherAI/gpt-neox-20b'):
+        self.tokenizer = GPTNeoXTokenizerFast.from_pretrained(model_name)
+        self.model = GPTNeoXForCausalLM.from_pretrained(model_name)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model.to(self.device)
 
@@ -246,20 +246,20 @@ class SOUL:
         return self.tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
 
     def bridge_ai(self, prompt):
-        # Generate the response using BLOOM
-        bloom_response = self.generate_text(prompt)
+        # Generate the response using GPT-NeoX
+        neox_response = self.generate_text(prompt)
 
         # Get the emotional response
-        emotional_response = get_emotional_response(bloom_response)
+        emotional_response = get_emotional_response(neox_response)
         
-        return bloom_response, emotional_response
+        return neox_response, emotional_response
 
 # Example usage of S.O.U.L. function
 soul = SOUL()
 
 def interact_with_soul(user_input):
-    bloom_response, emotional_response = soul.bridge_ai(user_input)
-    return bloom_response, emotional_response
+    neox_response, emotional_response = soul.bridge_ai(user_input)
+    return neox_response, emotional_response
 
 iface = gr.Interface(
     fn=interact_with_soul,