Update app.py

app.py

@@ -2,30 +2,25 @@ import streamlit as st
 from PIL import Image
 import torch
 from torchvision import transforms
-from your_model_library import YourModel  # Replace with actual model import
+from bk_sdm import BKSDMPipeline  # Hypothetical import; replace with actual model import
 
-# Load your lightweight model
-model = YourModel()
-model.load_state_dict(torch.load('path_to_model_weights.pth', map_location=torch.device('cpu')))
-model.eval()
+# Initialize the model
+@st.cache_resource
+def load_model():
+    model = BKSDMPipeline.from_pretrained('path_to_bk_sdm_model')
+    return model
 
-# Define image transformation
-transform = transforms.Compose([
-    transforms.Resize((256, 256)),
-    transforms.ToTensor(),
-])
+model = load_model()
 
-st.title("Text-to-Image Generator")
-
-# Input text prompt
+# Streamlit app interface
+st.title("Text-to-Image Generation")
 prompt = st.text_input("Enter a text prompt:")
 
 if st.button("Generate Image"):
     if prompt:
-        with torch.no_grad():
-            # Generate image from text prompt
-            generated_image = model.generate(prompt)  # Replace with actual generation method
-            generated_image = transform(generated_image).unsqueeze(0)
-            st.image(generated_image.squeeze().permute(1, 2, 0).numpy(), caption="Generated Image")
+        with st.spinner("Generating image..."):
+            # Generate image
+            image = model(prompt)
+            st.image(image, caption="Generated Image")
     else:
-        st.warning("Please enter a text prompt.")
+        st.warning("Please enter a prompt.")