changed model to preloaded in beginning

app.py

@@ -10,7 +10,6 @@ from openai import OpenAI
 import openai
 from diffusers import StableDiffusionPipeline
 
-
 # Initialize session state variables
 if 'simplified_text' not in st.session_state:
     st.session_state['simplified_text'] = ''
@@ -33,7 +32,9 @@ if 'image_from_simplified_text' not in st.session_state:
 if 'image_from_press_text' not in st.session_state:
     st.session_state['image_from_press_text'] = None
 
+######loading models########
 
+####loading simplifier model#####
 # Define model and tokenizer names for the text simplification model
 model_name = "mrm8488/t5-small-finetuned-text-simplification"
 tokenizer_name = "mrm8488/t5-small-finetuned-text-simplification"
@@ -47,7 +48,8 @@ if 'model' not in st.session_state or 'tokenizer' not in st.session_state:
 # Use the model from session state
 simplifier = st.session_state['simplifier']
 
-# Function to load CLIP model
+####loading clip model#####
+# Function to load the CLIP model
 def load_clip_model():
     model_clip, _, transform_clip = open_clip.create_model_and_transforms(
         model_name="coca_ViT-L-14",
@@ -55,31 +57,45 @@ def load_clip_model():
     )
     return model_clip, transform_clip
 
-# Function to generate a caption for the uploaded image
+if 'loaded_clip_model' not in st.session_state or 'loaded_transform_clip' not in st.session_state:
+    st.session_state['loaded_clip_model'], st.session_state['loaded_transform_clip'] = load_clip_model()
+
+# Function to generate a caption using the preloaded CLIP model
 def generate_caption(image_path):
-    # Load the CLIP model if it hasn't been loaded yet
-    if st.session_state['model_clip'] is None or st.session_state['transform_clip'] is None:
-        st.session_state['model_clip'], st.session_state['transform_clip'] = load_clip_model()
-    
-    # Load and preprocess the uploaded image
     im = Image.open(image_path).convert("RGB")
-    im = st.session_state['transform_clip'](im).unsqueeze(0)
+    im = st.session_state['loaded_transform_clip'](im).unsqueeze(0)
 
     # Generate a caption for the image
     with torch.no_grad(), torch.cuda.amp.autocast():
-        generated = st.session_state['model_clip'].generate(im)
+        generated = st.session_state['loaded_clip_model'].generate(im)
 
     new_caption = open_clip.decode(generated[0]).split("<end_of_text>")[0].replace("<start_of_text>", "")[:-2]
     return new_caption
 
+###loading diffusion model
+# Function to load the Stable Diffusion model
+def load_diffusion_model():
+    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
+    pipe = pipe.to("cuda")
+    return pipe
+
+# Initialize the model at the start and store it in the session state
+if 'loaded_model' not in st.session_state:
+    st.session_state['loaded_model'] = load_diffusion_model()
+
+# Function to generate an image using the preloaded model
+def generate_image(prompt):
+    image = st.session_state['loaded_model'](prompt).images[0]
+    return image
 
+################################################
 
 # Create a Streamlit app
-st.title("ARTSPEAK s i m p l i f i e r")
+st.title("ARTSPEAK  >  s i m p l i f i e r")
 
 # Display an image from the local file system
 logo_path = 'logo_artspeak.png'  # Replace with your image path
-st.image(logo_path, caption='Logo', use_column_width='auto')
+st.image(logo_path, use_column_width='auto')
 
 st.markdown("---")
 
@@ -118,14 +134,14 @@ if st.button("Simplify"):
 
 # Display the simplified text from session state
 if st.session_state['simplified_text']:
-    st.write("Simplified Text:")
+    st.write("Simplified Original Text:")
     st.write(st.session_state['simplified_text'])
 
 st.markdown("---")
 
 ####Get new caption
 # Modify the 'Get Caption' button section
-if st.button("Get Caption"):
+if st.button("Get New Caption"):
     if uploaded_image is not None:
         # Generate the caption
         caption = generate_caption(uploaded_image)
@@ -136,7 +152,7 @@ if st.button("Get Caption"):
 
 # Display the new caption from session state
 if st.session_state['new_caption']:
-    st.write("New Caption for Artwork:")
+    st.write("New Caption for this Artwork:")
     st.write(st.session_state['new_caption'])
 
 st.markdown("---")
@@ -178,7 +194,7 @@ if st.button("Generate Press Text from New Caption"):
 
 # Display the generated press text from new caption
 if st.session_state['message_content_from_caption']:
-    st.write("Generated Press Text from New Caption:")
+    st.write("Generated Press Text from New Caption of Artwork:")
     st.write(st.session_state['message_content_from_caption'])
 
 # Button to generate press text from simplified text
@@ -201,54 +217,37 @@ st.markdown("---")
 ############
 ##Diffusor##
 ############
-# Load Stable Diffusion model 
-def load_diffusion_model():
-    pipe = StableDiffusionPipeline.from_pretrained("runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16)
-    pipe = pipe.to("cuda")
-    return pipe
 
-# Function to generate an image and show a notification while processing
-def generate_image(pipe, prompt, notification_placeholder):
-    with notification_placeholder.container():
-        st.text('Generating image...')
-        image = pipe(prompt).images[0]
-        st.empty()  # Clear the notification after the process is done
-    return image
 
-# Button and notification for generating image from new caption
-if st.button("Generate Image from New Caption"):
-    notification_placeholder = st.empty()
+# Example button to generate image from new caption
+if st.button("Generate Image from New Caption of Artwork"):
     if st.session_state['new_caption']:
-        pipe = load_diffusion_model()
         prompt_caption = f"contemporary art of {st.session_state['new_caption']}"
-        st.session_state['image_from_caption'] = generate_image(pipe, prompt_caption, notification_placeholder)
+        st.session_state['image_from_caption'] = generate_image(prompt_caption)
 
 # Display the image generated from new caption
 if st.session_state['image_from_caption'] is not None:
     st.image(st.session_state['image_from_caption'], caption="Image from New Caption", use_column_width=True)
 
-
 # Button to generate image from simplified text
 if st.button("Generate Image from Simplified Text"):
-    notification_placeholder = st.empty()
     if st.session_state['simplified_text']:
-        pipe = load_diffusion_model()
         prompt_summary = f"contemporary art of {st.session_state['simplified_text']}"
-        st.session_state['image_from_simplified_text'] = generate_image(pipe, prompt_summary, notification_placeholder)
+        st.session_state['image_from_simplified_text'] = generate_image(pipe, prompt_summary)
 
 # Display the image generated from simplified text
 if st.session_state['image_from_simplified_text'] is not None:
     st.image(st.session_state['image_from_simplified_text'], caption="Image from Simplified Text", use_column_width=True)
 
-
 # Button to generate image from press text
-if st.button("Generate Image from Press Text"):
-    notification_placeholder = st.empty()
+if st.button("Generate Image from new Press Text"):
     if st.session_state['message_content_from_simplified_text']:
-        pipe = load_diffusion_model()
         prompt_press_text = f"contemporary art of {st.session_state['message_content_from_simplified_text']}"
-        st.session_state['image_from_press_text'] = generate_image(pipe, prompt_press_text, notification_placeholder)
+        st.session_state['image_from_press_text'] = generate_image(pipe, prompt_press_text)
 
 # Display the image generated from press text
 if st.session_state['image_from_press_text'] is not None:
-    st.image(st.session_state['image_from_press_text'], caption="Image from Press Text", use_column_width=True)
+    st.image(st.session_state['image_from_press_text'], caption="Image from Press Text", use_column_width=True)
+
+
+st.markdown("---")