Update app.py

app.py

@@ -2,25 +2,21 @@ from diffusers import StableDiffusionPipeline
 import torch
 from PIL import Image
 import streamlit as st
-import sys
-print(sys.version)
-
 
 # Load the Stable Diffusion pipeline
 @st.cache(allow_output_mutation=True)
 def load_pipeline():
     pipeline = StableDiffusionPipeline.from_pretrained(
-        "CompVis/stable-diffusion-v1-4", 
-        torch_dtype=torch.float32  # Change to float32 for CPU
+        "CompVis/stable-diffusion-v1-4",
+        torch_dtype=torch.float32  # Use float32 for CPU support
     )
     device = "cuda" if torch.cuda.is_available() else "cpu"
     pipeline.to(device)
     return pipeline
 
-
 def main():
     st.title("Stable Diffusion Image Generator")
-    st.write("Generate images from text prompts using Stable Diffusion 2.1")
+    st.write("Generate images from text prompts using Stable Diffusion")
 
     # Initialize the pipeline
     pipeline = load_pipeline()
@@ -29,19 +25,18 @@ def main():
     prompt = st.text_input("Enter your text prompt", "")
 
     # Generate button
-if st.button("Generate"):
-    if not prompt:
-        st.warning("Please enter a prompt first.")
-        return
+    if st.button("Generate"):
+        if not prompt:
+            st.warning("Please enter a prompt first.")
+            return  # ✅ Fixed indentation
 
-    st.write("Generating your image...")
-    with torch.no_grad():
-        result = pipeline(prompt)  # Returns a dictionary
-        image = result.images[0]  # Extract the first generated image
+        st.write("Generating your image...")
+        with torch.no_grad():
+            result = pipeline(prompt)
+            image = result.images[0]  # Extract the generated image
 
-    st.write("Generated Image:")
-    st.image(image, use_column_width=True)
-   
+        st.write("Generated Image:")
+        st.image(image, use_column_width=True)
 
 if __name__ == "__main__":
-    main()
+    main()