app.py

import streamlit as st
import torch
from diffusers import FluxPipeline
import io
import os

# Page configuration
st.set_page_config(page_title="Flux Image Generator", layout="centered")

st.title("🎨 AI Image Generator")
st.caption("Powered by FLUX.1 [schnell]")

# 1. THE LOAD FUNCTION
@st.cache_resource
def load_pipeline():
    # This pulls the secret you named 'HF_TOKEN' from your Space Settings
    token = os.getenv("HF_TOKEN")
    
    # Loading the model with the access token
    pipe = FluxPipeline.from_pretrained(
        "black-forest-labs/FLUX.1-schnell", 
        torch_dtype=torch.bfloat16,
        token=token
    )
    
    # Enables memory saving for the Hugging Face free tier
    pipe.enable_model_cpu_offload()
    return pipe

# Initialize the pipeline
try:
    pipeline = load_pipeline()
except Exception as e:
    st.error("Could not load the model. Make sure you accepted the terms on the model page and added your HF_TOKEN to secrets.")
    st.stop()

# 2. SIDEBAR SETTINGS
with st.sidebar:
    st.header("Settings")
    width = st.slider("Width", 512, 1024, 1024, step=128)
    height = st.slider("Height", 512, 1024, 1024, step=128)
    num_steps = st.slider("Inference Steps", 1, 4, 4)
    st.info("Tip: Use 4 steps for the best quality.")

# 3. USER INTERFACE
prompt = st.text_area("Enter your prompt:", "A futuristic robotic arm building a circuit board, cinematic lighting, 8k")

if st.button("Generate Image"):
    if prompt:
        with st.spinner("Generating... this may take a minute."):
            try:
                # Generate the image
                image = pipeline(
                    prompt,
                    num_inference_steps=num_steps,
                    guidance_scale=0.0,
                    width=width,
                    height=height,
                    max_sequence_length=256
                ).images[0]
                
                # Display image
                st.image(image, caption="Generated Result", use_column_width=True)
                
                # Download button
                buf = io.BytesIO()
                image.save(buf, format="PNG")
                byte_im = buf.getvalue()
                st.download_button(label="Download Image", data=byte_im, file_name="generated.png", mime="image/png")
            
            except Exception as e:
                st.error(f"Error during generation: {e}")
    else:
        st.warning("Please enter a prompt first!")