Create app.py

app.py

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+import streamlit as st
+from PIL import Image
+import io
+from diffusers import DiffusionPipeline
+
+# Load the diffusion pipeline
+@st.cache_resource
+def load_pipeline():
+    return DiffusionPipeline.from_pretrained("stabilityai/stable-diffusion-xl-refiner-1.0")
+
+pipe = load_pipeline()
+
+# Streamlit UI
+st.title("AI Image to Lego Minifigure Generator")
+
+st.write("Upload an image to convert it into a Lego minifigure styled image.")
+
+# File uploader for image upload
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Read the image
+    input_image = Image.open(uploaded_file)
+    
+    # Display the uploaded image
+    st.image(input_image, caption='Uploaded Image', use_column_width=True)
+    
+    # Prompt for the transformation
+    prompt = st.text_input("Enter a prompt for the transformation:", "Lego minifigure style")
+
+    if st.button("Generate Image"):
+        if prompt:
+            # Convert the image to bytes
+            buffered = io.BytesIO()
+            input_image.save(buffered, format="PNG")
+            input_image_bytes = buffered.getvalue()
+
+            # Generate the Lego minifigure styled image
+            with st.spinner("Generating image..."):
+                output_image = pipe(prompt).images[0]
+
+            # Display the transformed image
+            st.image(output_image, caption='Generated Lego Minifigure Styled Image', use_column_width=True)
+        else:
+            st.warning("Please enter a prompt for the transformation.")
+
+# Note: Remember to handle exceptions and edge cases in production code.