Create backup1-app.py

backup1-app.py

@@ -0,0 +1,279 @@
+import streamlit as st
+from gradio_client import Client
+import time
+import concurrent.futures
+import os
+from PIL import Image
+import io
+import requests
+
+# Get token from environment variable
+HF_TOKEN = os.getenv('ArtToken')
+if not HF_TOKEN:
+    raise ValueError("Please set the 'ArtToken' environment variable with your Hugging Face token")
+
+class ModelGenerator:
+    @staticmethod
+    def generate_midjourney(prompt):
+        try:
+            client = Client("mukaist/Midjourney", hf_token=HF_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"
+            )
+            
+            # Handle the result based on its type
+            if isinstance(result, list) and len(result) > 0:
+                # If result is a list of file paths or URLs
+                image_data = result[0]
+                if isinstance(image_data, str):
+                    if image_data.startswith('http'):
+                        # If it's a URL, download the image
+                        response = requests.get(image_data)
+                        image = Image.open(io.BytesIO(response.content))
+                    else:
+                        # If it's a file path
+                        image = Image.open(image_data)
+                else:
+                    # If it's already image data
+                    image = Image.open(io.BytesIO(image_data))
+                return ("Midjourney", image)
+            else:
+                return ("Midjourney", f"Error: Unexpected result format: {type(result)}")
+        except Exception as e:
+            return ("Midjourney", f"Error: {str(e)}")
+
+    @staticmethod
+    def generate_stable_cascade(prompt):
+        try:
+            client = Client("multimodalart/stable-cascade", hf_token=HF_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"
+            )
+            return ("Stable Cascade", result)
+        except Exception as e:
+            return ("Stable Cascade", f"Error: {str(e)}")
+
+    @staticmethod
+    def generate_stable_diffusion_3(prompt):
+        try:
+            client = Client("stabilityai/stable-diffusion-3-medium", hf_token=HF_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"
+            )
+            return ("SD 3 Medium", result)
+        except Exception as e:
+            return ("SD 3 Medium", f"Error: {str(e)}")
+
+    @staticmethod
+    def generate_stable_diffusion_35(prompt):
+        try:
+            client = Client("stabilityai/stable-diffusion-3.5-large", hf_token=HF_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"
+            )
+            return ("SD 3.5 Large", result)
+        except Exception as e:
+            return ("SD 3.5 Large", f"Error: {str(e)}")
+
+    @staticmethod
+    def generate_playground_v2_5(prompt):
+        try:
+            client = Client("https://playgroundai-playground-v2-5.hf.space/--replicas/ji5gy/", hf_token=HF_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"
+            )
+            # Result is a tuple (gallery, seed), we want just the first image from gallery
+            if result and isinstance(result, tuple) and result[0]:
+                return ("Playground v2.5", result[0][0]['image'])
+            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):
+    results = []
+    with concurrent.futures.ThreadPoolExecutor() as executor:
+        futures = []
+        model_map = {
+            "Midjourney": ModelGenerator.generate_midjourney,
+            "Stable Cascade": ModelGenerator.generate_stable_cascade,
+            "SD 3 Medium": ModelGenerator.generate_stable_diffusion_3,
+            "SD 3.5 Large": ModelGenerator.generate_stable_diffusion_35,
+            "Playground v2.5": ModelGenerator.generate_playground_v2_5
+        }
+        
+        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):
+            results.append(future.result())
+    
+    return results
+
+def handle_prompt_click(prompt_text, key):
+    if not HF_TOKEN:
+        st.error("Environment variable 'ArtToken' is not set!")
+        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")
+
+    with st.sidebar:
+        st.header("Configuration")
+        
+        # Show token status
+        if HF_TOKEN:
+            st.success("✓ ArtToken loaded from environment")
+        else:
+            st.error("⚠ ArtToken not found in environment")
+        
+        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
+        """)
+
+    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:
+                            # Updated to use use_container_width instead of use_column_width
+                            st.image(result, use_container_width=True)
+
+if __name__ == "__main__":
+    main()