Update app.py

app.py

@@ -11,6 +11,7 @@ from sklearn.neural_network import MLPClassifier
 from deap import base, creator, tools, algorithms
 from transformers import BloomForCausalLM, BloomTokenizerFast
 import torch
+import torch.multiprocessing as mp
 
 # Initialize Example Emotions Dataset
 data = {
@@ -223,7 +224,7 @@ def handle_idle_state():
 class SOUL:
     def __init__(self, model_name='bigscience/bloom-1b1'):
         self.tokenizer = BloomTokenizerFast.from_pretrained(model_name)
-        self.model = BloomForCausalLM.from_pretrained(model_name)
+        self.model = BloomForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16)
         self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
         self.model.to(self.device)
 
@@ -261,12 +262,20 @@ def interact_with_soul(user_input):
     bloom_response, emotional_response = soul.bridge_ai(user_input)
     return bloom_response, emotional_response
 
-iface = gr.Interface(
-    fn=interact_with_soul,
-    inputs="text",
-    outputs=["text", "text"],
-    title="S.O.U.L AI",
-    description="Enter a prompt to interact with the S.O.U.L AI, which will generate a response and provide an emotional analysis."
-)
-
-iface.launch()
+# Function to handle Gradio interface using multiprocessing
+def launch_gradio():
+    iface = gr.Interface(
+        fn=interact_with_soul,
+        inputs="text",
+        outputs=["text", "text"],
+        title="S.O.U.L AI",
+        description="Enter a prompt to interact with the S.O.U.L AI, which will generate a response and provide an emotional analysis."
+    )
+    iface.launch()
+
+# Use multiprocessing to utilize all CPU cores
+if __name__ == '__main__':
+    mp.set_start_method('spawn')
+    p = mp.Process(target=launch_gradio)
+    p.start()
+    p.join()