Commented out image model code

app.py

@@ -2,7 +2,7 @@ import streamlit as st
 from flask.Emotion_spotting_service import _Emotion_spotting_service
 from flask.Genre_spotting_service import _Genre_spotting_service
 from flask.Beat_tracking_service import _Beat_tracking_service
-from diffusers import StableDiffusionPipeline
+#from diffusers import StableDiffusionPipeline
 import torch
 
 emo_list = []
@@ -22,11 +22,11 @@ def load_beat_model():
     beat_service = _Beat_tracking_service()
     return beat_service
 
-@st.cache_resource
-def load_image_model():
-     pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",torch_dtype=torch.float16).to("cuda")
-     pipeline.load_lora_weights("Weights/pytorch_lora_weights.safetensors", weight_name="pytorch_lora_weights.safetensors")
-     return pipeline
+# @st.cache_resource
+# def load_image_model():
+#      pipeline = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5",torch_dtype=torch.float16).to("cuda")
+#      pipeline.load_lora_weights("Weights/pytorch_lora_weights.safetensors", weight_name="pytorch_lora_weights.safetensors")
+#      return pipeline
 
 
 if 'emotion' not in st.session_state:
@@ -41,7 +41,7 @@ if 'beat' not in st.session_state:
 emotion_service = load_emo_model()
 genre_service = load_genre_model()
 beat_service = load_beat_model()
-image_service = load_image_model()
+# image_service = load_image_model()
 
 st.title("Music2Image webpage")
 user_input = st.file_uploader("Upload your wav/mp3 files here", type=["wav","mp3"],key = "file_uploader")