import os
import io
import warnings
from PIL import Image
from stability_sdk import client
import stability_sdk.interfaces.gooseai.generation.generation_pb2 as generation

# Our Host URL should not be prepended with "https" nor should it have a trailing slash.
os.environ['STABILITY_HOST'] = 'grpc.stability.ai:443'

# Sign up for an account at the following link to get an API Key.
# https://platform.stability.ai/
# Click on the following link once you have created an account to be taken to your API Key.
# https://platform.stability.ai/account/keys

# Paste your API Key below.
os.environ['STABILITY_KEY'] = 'sk-KGCeQFf4iQYogzAe6WEISIOij12g4Ztvnkw92dJTJZ7vsL0j'

def generate_stable_diffusion_image(prompt):

    # Set up our connection to the API.
    # Check out the following link for a list of available engines: 
    # https://platform.stability.ai/docs/features/api-parameters#engine
    stability_api = client.StabilityInference(
        key=os.environ['STABILITY_KEY'], # API Key reference.
        verbose=True, # Print debug messages.
        engine="stable-diffusion-xl-1024-v1-0", # Set the engine to use for generation.
    )

    # Set up our initial generation parameters.
    answers = stability_api.generate(
        prompt=prompt,
        seed=4253978046, # If a seed is provided, the resulting generated image will be deterministic.
        # What this means is that as long as all generation parameters remain the same, 
        # you can always recall the same image simply by generating it again.
        # Note: This isn't quite the case for Clip Guided generations, which we'll tackle in a future example notebook.
        steps=50, # Amount of inference steps performed on image generation. Defaults to 30.
        cfg_scale=7.0, 
        # Influences how strongly your generation is guided to match your prompt.
        # Setting this value higher increases the strength in which it tries to match your prompt.
        # Defaults to 7.0 if not specified.
        width=1024, # Generation width, defaults to 512 if not included.
        height=1024, # Generation height, defaults to 512 if not included.
        samples=1, # Number of images to generate, defaults to 1 if not included.
        sampler=generation.SAMPLER_K_DPMPP_2M 
        # Choose which sampler we want to denoise our generation with.
        # Defaults to k_dpmpp_2m if not specified. Clip Guidance only supports ancestral samplers.
        # (Available Samplers: ddim, plms, k_euler, k_euler_ancestral, k_heun, k_dpm_2, k_dpm_2_ancestral, 
        # k_dpmpp_2s_ancestral, k_lms, k_dpmpp_2m, k_dpmpp_sde)
    )

    # Set up our warning to print to the console if the adult content classifier is tripped.
    # If adult content classifier is not tripped, save generated images.
    for resp in answers:
        for artifact in resp.artifacts:
            if artifact.finish_reason == generation.FILTER:
                warnings.warn(
                    "Your request activated the API's safety filters and could not be processed."
                    "Please modify the prompt and try again.")
            if artifact.type == generation.ARTIFACT_IMAGE:
                img = Image.open(io.BytesIO(artifact.binary))
                img_name = image_dir + str(artifact.seed) + ".png"
                img.show()
                img.save(img_name) 
                # Save our generated images with their seed number as the filename.