Removed image model

app.py

@@ -2,11 +2,10 @@ 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
 import os
 
-os.environ['CUDA_VISIBLE_DEVICES'] = '4'
 emo_list = []
 gen_list = []
 tempo_list = []
@@ -24,11 +23,11 @@ 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:
@@ -43,7 +42,7 @@ 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")
@@ -73,7 +72,7 @@ if st.session_state.emotion != None and st.session_state.genre != None and st.se
         st.caption("Text description of your music file")
         text_output = "This piece of music falls under the " + st.session_state.genre[0] + " genre. It is of tempo " + str(int(st.session_state.beat)) + " and evokes a sense of" + st.session_state.emotion + "."
         st.text(text_output)
-    if text_output:
-       if st.button("Generate image from text description"):
-            image = image_service(text_output)
-            st.image(image)
+    # if text_output:
+    #    if st.button("Generate image from text description"):
+    #         image = image_service(text_output)
+    #         st.image(image)