import requests
import io
from PIL import Image
import gradio as gr
import os

# Ensure your API token is correctly set in your environment variables
API_TOKEN = os.getenv("HF_API_TOKEN")
if not API_TOKEN:
    raise ValueError("Hugging Face API token not found. Please set the HF_API_TOKEN environment variable.")

API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-xl-base-1.0"
headers = {"Authorization": f"Bearer {API_TOKEN}"}

def generate_image(prompt, negative_prompt="", guidance_scale=7.5, width=1024, height=768, num_inference_steps=30):
    payload = {
        "inputs": prompt,
        "parameters": {
            "guidance_scale": guidance_scale,
            "width": width,
            "height": height,
            "num_inference_steps": num_inference_steps,
        },
    }
    if negative_prompt:  # Only add negative_prompt to payload if it's provided
        payload["parameters"]["negative_prompt"] = negative_prompt

    response = requests.post(API_URL, headers=headers, json=payload)
    image_bytes = response.content
    image = Image.open(io.BytesIO(image_bytes))
    return image

iface = gr.Interface(
    fn=generate_image,
    inputs=[
        gr.Textbox(label="Prompt", placeholder="Enter your prompt here..."),
        gr.Textbox(label="Negative Prompt", placeholder="Enter a negative prompt here (optional)..."),
        gr.Slider(label="Guidance Scale", minimum=1, maximum=20, step=0.1, value=7.5),
        gr.Slider(label="Width", minimum=768, maximum=1024, step=1, value=1024),
        gr.Slider(label="Height", minimum=768, maximum=1024, step=1, value=768),
        gr.Slider(label="Number of Inference Steps", minimum=20, maximum=50, step=1, value=30)
    ],
    outputs=gr.Image(type="pil"),
    title="Stable Diffusion XL Image Generator",
    description="Generate images with Stable Diffusion XL. Provide a prompt, optionally specify a negative prompt, and adjust other parameters as desired."
)

iface.launch()