app.py

import streamlit as st
from gradio_client import Client
import time
import concurrent.futures
import os
from PIL import Image
import io
import requests
from huggingface_hub import HfApi, login

# Initialize session state - must be first
if 'hf_token' not in st.session_state:
    st.session_state['hf_token'] = None
if 'is_authenticated' not in st.session_state:
    st.session_state['is_authenticated'] = False

class ModelGenerator:
    @staticmethod
    def generate_midjourney(prompt, token):
        try:
            client = Client("mukaist/Midjourney", hf_token=token)
            result = client.predict(
                prompt=prompt,
                negative_prompt="(deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime:1.4), text, close up, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck",
                use_negative_prompt=True,
                style="2560 x 1440",
                seed=0,
                width=1024,
                height=1024,
                guidance_scale=6,
                randomize_seed=True,
                api_name="/run"
            )
            
            if isinstance(result, tuple):
                image_data = result[0] if len(result) > 0 else None
            elif isinstance(result, list):
                image_data = result[0] if len(result) > 0 else None
            else:
                image_data = result

            if image_data:
                if isinstance(image_data, str):
                    if image_data.startswith('http'):
                        response = requests.get(image_data)
                        return ("Midjourney", Image.open(io.BytesIO(response.content)))
                    return ("Midjourney", Image.open(image_data))
                elif isinstance(image_data, bytes):
                    return ("Midjourney", Image.open(io.BytesIO(image_data)))
                elif hasattr(image_data, 'read'):  # File-like object
                    return ("Midjourney", Image.open(image_data))
            return ("Midjourney", "Error: No valid image data found")
        except Exception as e:
            return ("Midjourney", f"Error: {str(e)}")

    @staticmethod
    def generate_stable_cascade(prompt, token):
        try:
            client = Client("multimodalart/stable-cascade", hf_token=token)
            result = client.predict(
                prompt=prompt,
                negative_prompt=prompt,
                seed=0,
                width=1024,
                height=1024,
                prior_num_inference_steps=20,
                prior_guidance_scale=4,
                decoder_num_inference_steps=10,
                decoder_guidance_scale=0,
                num_images_per_prompt=1,
                api_name="/run"
            )
            if isinstance(result, (str, bytes)):
                return ("Stable Cascade", Image.open(io.BytesIO(result) if isinstance(result, bytes) else result))
            elif isinstance(result, list) and len(result) > 0:
                return ("Stable Cascade", Image.open(io.BytesIO(result[0]) if isinstance(result[0], bytes) else result[0]))
            return ("Stable Cascade", "Error: No valid image data found")
        except Exception as e:
            return ("Stable Cascade", f"Error: {str(e)}")

    @staticmethod
    def generate_stable_diffusion_3(prompt, token):
        try:
            client = Client("stabilityai/stable-diffusion-3-medium", hf_token=token)
            result = client.predict(
                prompt=prompt,
                negative_prompt=prompt,
                seed=0,
                randomize_seed=True,
                width=1024,
                height=1024,
                guidance_scale=5,
                num_inference_steps=28,
                api_name="/infer"
            )
            if isinstance(result, bytes):
                return ("SD 3 Medium", Image.open(io.BytesIO(result)))
            elif isinstance(result, str):
                if result.startswith('http'):
                    response = requests.get(result)
                    return ("SD 3 Medium", Image.open(io.BytesIO(response.content)))
                return ("SD 3 Medium", Image.open(result))
            elif isinstance(result, list) and len(result) > 0:
                image_data = result[0]
                if isinstance(image_data, bytes):
                    return ("SD 3 Medium", Image.open(io.BytesIO(image_data)))
                elif isinstance(image_data, str):
                    if image_data.startswith('http'):
                        response = requests.get(image_data)
                        return ("SD 3 Medium", Image.open(io.BytesIO(response.content)))
                    return ("SD 3 Medium", Image.open(image_data))
            return ("SD 3 Medium", "Error: No valid image data found")
        except Exception as e:
            return ("SD 3 Medium", f"Error: {str(e)}")

    @staticmethod
    def generate_stable_diffusion_35(prompt, token):
        try:
            client = Client("stabilityai/stable-diffusion-3.5-large", hf_token=token)
            result = client.predict(
                prompt=prompt,
                negative_prompt=prompt,
                seed=0,
                randomize_seed=True,
                width=1024,
                height=1024,
                guidance_scale=4.5,
                num_inference_steps=40,
                api_name="/infer"
            )
            if isinstance(result, bytes):
                return ("SD 3.5 Large", Image.open(io.BytesIO(result)))
            elif isinstance(result, str):
                if result.startswith('http'):
                    response = requests.get(result)
                    return ("SD 3.5 Large", Image.open(io.BytesIO(response.content)))
                return ("SD 3.5 Large", Image.open(result))
            elif isinstance(result, list) and len(result) > 0:
                image_data = result[0]
                if isinstance(image_data, bytes):
                    return ("SD 3.5 Large", Image.open(io.BytesIO(image_data)))
                elif isinstance(image_data, str):
                    if image_data.startswith('http'):
                        response = requests.get(image_data)
                        return ("SD 3.5 Large", Image.open(io.BytesIO(response.content)))
                    return ("SD 3.5 Large", Image.open(image_data))
            return ("SD 3.5 Large", "Error: No valid image data found")
        except Exception as e:
            return ("SD 3.5 Large", f"Error: {str(e)}")

    @staticmethod
    def generate_playground_v2_5(prompt, token):
        try:
            client = Client("https://playgroundai-playground-v2-5.hf.space/--replicas/ji5gy/", 
                          hf_token=token)
            result = client.predict(
                prompt,
                prompt,  # negative prompt
                True,    # use negative prompt
                0,      # seed
                1024,   # width
                1024,   # height
                7.5,    # guidance scale
                True,   # randomize seed
                api_name="/run"
            )
            if isinstance(result, tuple) and result[0] and len(result[0]) > 0:
                image_data = result[0][0].get('image')
                if image_data:
                    if isinstance(image_data, str):
                        if image_data.startswith('http'):
                            response = requests.get(image_data)
                            return ("Playground v2.5", Image.open(io.BytesIO(response.content)))
                        return ("Playground v2.5", Image.open(image_data))
                    return ("Playground v2.5", Image.open(io.BytesIO(image_data)))
            return ("Playground v2.5", "Error: No image generated")
        except Exception as e:
            return ("Playground v2.5", f"Error: {str(e)}")

def generate_images(prompt, selected_models):
    token = st.session_state.get('hf_token')
    if not token:
        return [("Error", "No authentication token found")]
        
    results = []
    with concurrent.futures.ThreadPoolExecutor() as executor:
        futures = []
        model_map = {
            "Midjourney": lambda p: ModelGenerator.generate_midjourney(p, token),
            "Stable Cascade": lambda p: ModelGenerator.generate_stable_cascade(p, token),
            "SD 3 Medium": lambda p: ModelGenerator.generate_stable_diffusion_3(p, token),
            "SD 3.5 Large": lambda p: ModelGenerator.generate_stable_diffusion_35(p, token),
            "Playground v2.5": lambda p: ModelGenerator.generate_playground_v2_5(p, token)
        }
        
        for model in selected_models:
            if model in model_map:
                futures.append(executor.submit(model_map[model], prompt))
        
        for future in concurrent.futures.as_completed(futures):
            try:
                result = future.result()
                if result:
                    results.append(result)
            except Exception as e:
                st.error(f"Error during image generation: {str(e)}")
    
    return results

def handle_prompt_click(prompt_text, key):
    if not st.session_state.get('is_authenticated') or not st.session_state.get('hf_token'):
        st.error("Please login with your HuggingFace account first!")
        return
        
    st.session_state[f'selected_prompt_{key}'] = prompt_text
    
    selected_models = st.session_state.get('selected_models', [])
    
    if not selected_models:
        st.warning("Please select at least one model from the sidebar!")
        return

    with st.spinner('Generating artwork...'):
        results = generate_images(prompt_text, selected_models)
        st.session_state[f'generated_images_{key}'] = results
        st.success("Artwork generated successfully!")

def main():
    st.title("🎨 Multi-Model Art Generator")

    # Handle authentication in sidebar
    with st.sidebar:
        st.header("🔐 Authentication")
        if st.session_state.get('is_authenticated') and st.session_state.get('hf_token'):
            st.success("✓ Logged in to HuggingFace")
            if st.button("Logout"):
                st.session_state['hf_token'] = None
                st.session_state['is_authenticated'] = False
                st.rerun()
        else:
            token = st.text_input("Enter HuggingFace Token", type="password",
                                help="Get your token from https://huggingface.co/settings/tokens")
            if st.button("Login"):
                if token:
                    try:
                        # Verify token is valid
                        api = HfApi(token=token)
                        api.whoami()
                        st.session_state['hf_token'] = token
                        st.session_state['is_authenticated'] = True
                        st.success("Successfully logged in!")
                        st.rerun()
                    except Exception as e:
                        st.error(f"Authentication failed: {str(e)}")
                else:
                    st.error("Please enter your HuggingFace token")
        
        if st.session_state.get('is_authenticated') and st.session_state.get('hf_token'):
            st.markdown("---")
            st.header("Model Selection")
            st.session_state['selected_models'] = st.multiselect(
                "Choose AI Models",
                ["Midjourney", "Stable Cascade", "SD 3 Medium", "SD 3.5 Large", "Playground v2.5"],
                default=["Midjourney"]
            )
            
            st.markdown("---")
            st.markdown("### Selected Models:")
            for model in st.session_state['selected_models']:
                st.write(f"✓ {model}")
            
            st.markdown("---")
            st.markdown("### Model Information:")
            st.markdown("""
            - **Midjourney**: Best for artistic and creative imagery
            - **Stable Cascade**: New architecture with high detail
            - **SD 3 Medium**: Fast and efficient generation
            - **SD 3.5 Large**: Highest quality, slower generation
            - **Playground v2.5**: Advanced model with high customization
            """)

    # Only show the main interface if authenticated
    if st.session_state.get('is_authenticated') and st.session_state.get('hf_token'):
        st.markdown("### Select a prompt style to generate artwork:")

        prompt_emojis = {
            "AIart/AIArtistCommunity": "🤖",
            "Black & White": "⚫⚪",
            "Black & Yellow": "⚫💛",
            "Blindfold": "🙈",
            "Break": "💔",
            "Broken": "🔨",
            "Christmas Celebrations art": "🎄",
            "Colorful Art": "🎨",
            "Crimson art": "🔴",
            "Eyes Art": "👁️",
            "Going out with Style": "💃",
            "Hooded Girl": "🧥",
            "Lips": "👄",
            "MAEKHLONG": "🏮",
            "Mermaid": "🧜‍♀️",
            "Morning Sunshine": "🌅",
            "Music Art": "🎵",
            "Owl": "🦉",
            "Pink": "💗",
            "Purple": "💜",
            "Rain": "🌧️",
            "Red Moon": "🌑",
            "Rose": "🌹",
            "Snow": "❄️",
            "Spacesuit Girl": "👩‍🚀",
            "Steampunk": "⚙️",
            "Succubus": "😈",
            "Sunlight": "☀️",
            "Weird art": "🎭",
            "White Hair": "👱‍♀️",
            "Wings art": "👼",
            "Woman with Sword": "⚔️"
        }

        col1, col2, col3 = st.columns(3)
        
        for idx, (prompt, emoji) in enumerate(prompt_emojis.items()):
            full_prompt = f"QT {prompt}"
            col = [col1, col2, col3][idx % 3]
            
            with col:
                if st.button(f"{emoji} {prompt}", key=f"btn_{idx}"):
                    handle_prompt_click(full_prompt, idx)

        st.markdown("---")
        st.markdown("### Generated Artwork:")
        
        for key in st.session_state:
            if key.startswith('selected_prompt_'):
                idx = key.split('_')[-1]
                images_key = f'generated_images_{idx}'
                
                if images_key in st.session_state:
                    st.write("Prompt:", st.session_state[key])
                    
                    cols = st.columns(len(st.session_state[images_key]))
                    
                    for col, (model_name, result) in zip(cols, st.session_state[images_key]):
                        with col:
                            st.markdown(f"**{model_name}**")
                            if isinstance(result, str) and result.startswith("Error"):
                                st.error(result)
                            else:
                                st.image(result, use_container_width=True)
    else:
        st.info("Please login with your HuggingFace account to use the app")

if __name__ == "__main__":
    main()