Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 import numpy as np
-import spaces
+import spaces # This is a special module for Hugging Face Spaces, not needed for local execution
 import torch
 import random
 import json
@@ -8,415 +8,276 @@ import os
 from PIL import Image
 from diffusers import FluxKontextPipeline
 from diffusers.utils import load_image
-from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard, list_repo_files
+from huggingface_hub import hf_hub_download, HfFileSystem, ModelCard
 from safetensors.torch import load_file
 import requests
 import re
 
-# Load Kontext model
+# Load Kontext model from your local path
 MAX_SEED = np.iinfo(np.int32).max
 
-pipe = FluxKontextPipeline.from_pretrained("black-forest-labs/FLUX.1-Kontext-dev", torch_dtype=torch.bfloat16).to("cuda")
+# Use the local path for the base model as in your test.py
+pipe = FluxKontextPipeline.from_pretrained(
+    "black-forest-labs/FLUX.1-Kontext-dev", 
+    torch_dtype=torch.bfloat16
+).to("cuda")
 
-# Load LoRA data
-flux_loras_raw = [
-    {
-        "image": "https://huggingface.co/fal/Realism-Detailer-Kontext-Dev-LoRA/resolve/main/outputs/1.png",
-        "title": "Realism Detailer Kontext",
-        "repo": "fal/Realism-Detailer-Kontext-Dev-LoRA",
-        "trigger_word": "Add details to this face, improve skin details",
-        "weights": "high_detail.safetensors"
-    },
-]
-print(f"Loaded {len(flux_loras_raw)} LoRAs")
-# Global variables for LoRA management
-current_lora = None
-lora_cache = {}
-
-def load_lora_weights(repo_id, weights_filename):
-    """Load LoRA weights from HuggingFace"""
-    try:
-        # First try with the specified filename
-        try:
-            lora_path = hf_hub_download(repo_id=repo_id, filename=weights_filename)
-            if repo_id not in lora_cache:
-                lora_cache[repo_id] = lora_path
-            return lora_path
-        except Exception as e:
-            print(f"Failed to load {weights_filename}, trying to find alternative LoRA files...")
-            
-            # If the specified file doesn't exist, try to find any .safetensors file
-            from huggingface_hub import list_repo_files
-            try:
-                files = list_repo_files(repo_id)
-                safetensors_files = [f for f in files if f.endswith(('.safetensors', '.bin')) and 'lora' in f.lower()]
-                
-                if not safetensors_files:
-                    # Try without 'lora' in filename
-                    safetensors_files = [f for f in files if f.endswith('.safetensors')]
-                
-                if safetensors_files:
-                    # Try the first available file
-                    for file in safetensors_files:
-                        try:
-                            print(f"Trying alternative file: {file}")
-                            lora_path = hf_hub_download(repo_id=repo_id, filename=file)
-                            if repo_id not in lora_cache:
-                                lora_cache[repo_id] = lora_path
-                            print(f"Successfully loaded alternative LoRA file: {file}")
-                            return lora_path
-                        except:
-                            continue
-                            
-                print(f"No suitable LoRA files found in {repo_id}")
-                return None
-                
-            except Exception as list_error:
-                print(f"Error listing files in repo {repo_id}: {list_error}")
-                return None
-                
-    except Exception as e:
-        print(f"Error loading LoRA from {repo_id}: {e}")
-        return None
+# Load LoRA data from our custom JSON file
+with open("kontext_loras.json", "r") as file:
+    data = json.load(file)
+    # Add default values for keys that might be missing, to prevent errors
+    flux_loras_raw = [
+        {
+            "image": item["image"],
+            "title": item["title"],
+            "repo": item["repo"],
+            "weights": item.get("weights", "pytorch_lora_weights.safetensors"),
+            "prompt": item.get("prompt", f"Turn this image into {item['title']} style."),
+            # The following keys are kept for compatibility with the original demo structure,
+            # but our simplified logic doesn't heavily rely on them.
+            "lora_type": item.get("lora_type", "flux"),
+            "lora_scale_config": item.get("lora_scale", 1.0), # Default scale set to 1.0
+            "prompt_placeholder": item.get("prompt_placeholder", "You can edit the prompt here..."),
+        }
+        for item in data
+    ]
+print(f"Loaded {len(flux_loras_raw)} LoRAs from kontext_loras.json")
 
 def update_selection(selected_state: gr.SelectData, flux_loras):
     """Update UI when a LoRA is selected"""
     if selected_state.index >= len(flux_loras):
-        return "### No LoRA selected", gr.update(), None
-    
-    lora = flux_loras[selected_state.index]
-    lora_title = lora["title"]
-    lora_repo = lora["repo"]
-    trigger_word = lora["trigger_word"]
+        return "### No LoRA selected", gr.update(), None, gr.update()
     
-    # Create a more informative selected text
-    updated_text = f"### 🎨 Selected Style: {lora_title}"
-    new_placeholder = f"Describe additional details, e.g., 'wearing a red hat' or 'smiling'"
+    selected_lora = flux_loras[selected_state.index]
+    lora_repo = selected_lora["repo"]
+    default_prompt = selected_lora.get("prompt")
     
-    return updated_text, gr.update(placeholder=new_placeholder), selected_state.index
+    updated_text = f"### Selected: [{lora_repo}](https://huggingface.co/{lora_repo})"
 
-def get_huggingface_lora(link):
-    """Download LoRA from HuggingFace link"""
-    split_link = link.split("/")
-    if len(split_link) == 2:
-        try:
-            model_card = ModelCard.load(link)
-            trigger_word = model_card.data.get("instance_prompt", "")
-            
-            # Try to find the correct safetensors file
-            files = list_repo_files(link)
-            safetensors_files = [f for f in files if f.endswith('.safetensors')]
-            
-            # Prioritize files with 'lora' in the name
-            lora_files = [f for f in safetensors_files if 'lora' in f.lower()]
-            if lora_files:
-                safetensors_file = lora_files[0]
-            elif safetensors_files:
-                safetensors_file = safetensors_files[0]
-            else:
-                # Try .bin files as fallback
-                bin_files = [f for f in files if f.endswith('.bin') and 'lora' in f.lower()]
-                if bin_files:
-                    safetensors_file = bin_files[0]
-                else:
-                    safetensors_file = "pytorch_lora_weights.safetensors"  # Default fallback
-            
-            print(f"Found LoRA file: {safetensors_file} in {link}")
-            return split_link[1], safetensors_file, trigger_word
-            
-        except Exception as e:
-            print(f"Error in get_huggingface_lora: {e}")
-            # Try basic detection
-            try:
-                files = list_repo_files(link)
-                safetensors_file = next((f for f in files if f.endswith('.safetensors')), "pytorch_lora_weights.safetensors")
-                return split_link[1], safetensors_file, ""
-            except:
-                raise Exception(f"Error loading LoRA: {e}")
-    else:
-        raise Exception("Invalid HuggingFace repository format")
-
-def load_custom_lora(link):
-    """Load custom LoRA from user input"""
-    if not link:
-        return gr.update(visible=False), "", gr.update(visible=False), None, gr.Gallery(selected_index=None), "### 🎨 Select an art style from the gallery", None
-    
-    try:
-        repo_name, weights_file, trigger_word = get_huggingface_lora(link)
-        
-        card = f'''
-        <div class="custom_lora_card">
-            <div style="display: flex; align-items: center; margin-bottom: 12px;">
-                <span style="font-size: 18px; margin-right: 8px;">✅</span>
-                <strong style="font-size: 16px;">Custom LoRA Loaded!</strong>
-            </div>
-            <div style="background: rgba(255, 255, 255, 0.8); padding: 12px; border-radius: 8px;">
-                <h4 style="margin: 0 0 8px 0; color: #333;">{repo_name}</h4>
-                <small style="color: #666;">{"Trigger: <code style='background: #f0f0f0; padding: 2px 6px; border-radius: 4px;'><b>"+trigger_word+"</b></code>" if trigger_word else "No trigger word found"}</small>
-            </div>
-        </div>
-        '''
-        
-        custom_lora_data = {
-            "repo": link,
-            "weights": weights_file,
-            "trigger_word": trigger_word
-        }
-        
-        return gr.update(visible=True), card, gr.update(visible=True), custom_lora_data, gr.Gallery(selected_index=None), f"🎨 Custom Style: {repo_name}", None
-    
-    except Exception as e:
-        return gr.update(visible=True), f"Error: {str(e)}", gr.update(visible=False), None, gr.update(), "### 🎨 Select an art style from the gallery", None
-
-def remove_custom_lora():
-    """Remove custom LoRA"""
-    return "", gr.update(visible=False), gr.update(visible=False), None, None
+    optimal_scale = selected_lora.get("lora_scale_config", 1.0)
+    print("Selected Style: ", selected_lora['title'])
+    print("Optimal Scale: ", optimal_scale)
+    return updated_text, gr.update(value=default_prompt), selected_state.index, optimal_scale
 
-def classify_gallery(flux_loras):
-    """Sort gallery by likes"""
-    try:
-        sorted_gallery = sorted(flux_loras, key=lambda x: x.get("likes", 0), reverse=True)
-        gallery_items = []
-        
-        for item in sorted_gallery:
-            if "image" in item and "title" in item:
-                image_path = item["image"]
-                title = item["title"]
-                
-                # Simply use the path as-is for Gradio to handle
-                gallery_items.append((image_path, title))
-                print(f"Added to gallery: {image_path} - {title}")
-        
-        print(f"Total gallery items: {len(gallery_items)}")
-        return gallery_items, sorted_gallery
-    except Exception as e:
-        print(f"Error in classify_gallery: {e}")
-        import traceback
-        traceback.print_exc()
-        return [], []
-
-def infer_with_lora_wrapper(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
+# This wrapper is kept for compatibility with the Gradio event triggers
+def infer_with_lora_wrapper(input_image, prompt, selected_index, lora_state, custom_lora, seed=0, guidance_scale=2.5, num_inference_steps=28, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
     """Wrapper function to handle state serialization"""
-    return infer_with_lora(input_image, prompt, selected_index, custom_lora, seed, randomize_seed, guidance_scale, lora_scale, flux_loras, progress)
+    # The 'custom_lora' and 'lora_state' arguments are no longer used but kept in the signature
+    return infer_with_lora(input_image, prompt, selected_index, seed, guidance_scale, num_inference_steps, lora_scale, flux_loras, progress)
 
-@spaces.GPU
-def infer_with_lora(input_image, prompt, selected_index, custom_lora, seed=42, randomize_seed=False, guidance_scale=2.5, lora_scale=1.0, flux_loras=None, progress=gr.Progress(track_tqdm=True)):
+@spaces.GPU # This decorator is only for Hugging Face Spaces hardware, not needed for local execution
+def infer_with_lora(input_image, prompt, selected_index, seed=0, guidance_scale=2.5, num_inference_steps=28, lora_scale=1.0, flux_loras=None,  progress=gr.Progress(track_tqdm=True)):
     """Generate image with selected LoRA"""
-    global current_lora, pipe
+    global pipe
     
-    # Check if input image is provided
-    if input_image is None:
-        gr.Warning("Please upload your portrait photo first! 📸")
-        return None, seed, gr.update(visible=False)
+    # The seed is now always taken directly from the input. Randomization has been removed.
     
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
+    # Unload any previous LoRA to ensure a clean state
+    if "selected_lora" in pipe.get_active_adapters():
+        pipe.unload_lora_weights()
     
-    # Determine which LoRA to use
+    # Determine which LoRA to use from our gallery
     lora_to_use = None
-    if custom_lora:
-        lora_to_use = custom_lora
-    elif selected_index is not None and flux_loras and selected_index < len(flux_loras):
+    if selected_index is not None and flux_loras and selected_index < len(flux_loras):
         lora_to_use = flux_loras[selected_index]
-    # Load LoRA if needed
-    if lora_to_use and lora_to_use != current_lora:
+    
+    if lora_to_use:
+        print(f"Applying LoRA: {lora_to_use['title']}")
         try:
-            # Unload current LoRA
-            if current_lora:
-                pipe.unload_lora_weights()
-                print(f"Unloaded previous LoRA")
-            
-            # Load new LoRA
-            repo_id = lora_to_use.get("repo", "unknown")
-            weights_file = lora_to_use.get("weights", "pytorch_lora_weights.safetensors")
-            print(f"Loading LoRA: {repo_id} with weights: {weights_file}")
-            
-            lora_path = load_lora_weights(repo_id, weights_file)
-            if lora_path:
-                pipe.load_lora_weights(lora_path, adapter_name="selected_lora")
-                pipe.set_adapters(["selected_lora"], adapter_weights=[lora_scale])
-                print(f"Successfully loaded: {lora_path} with scale {lora_scale}")
-                current_lora = lora_to_use
-            else:
-                print(f"Failed to load LoRA from {repo_id}")
-                gr.Warning(f"Failed to load {lora_to_use.get('title', 'style')}. Please try a different art style.")
-                return None, seed, gr.update(visible=False)
+            # Load LoRA directly from the Hugging Face Hub
+            pipe.load_lora_weights(
+                lora_to_use["repo"], 
+                weight_name=lora_to_use["weights"], 
+                adapter_name="selected_lora"
+            )
+            pipe.set_adapters(["selected_lora"], adapter_weights=[lora_scale])
+            print(f"Loaded {lora_to_use['repo']} with scale {lora_scale}")
             
         except Exception as e:
             print(f"Error loading LoRA: {e}")
-            # Continue without LoRA
-    else:
-        if lora_to_use:
-            print(f"Using already loaded LoRA: {lora_to_use.get('repo', 'unknown')}")
-    
-    try:
-        # Convert image to RGB
-        input_image = input_image.convert("RGB")
-    except Exception as e:
-        print(f"Error processing image: {e}")
-        gr.Warning("Error processing the uploaded image. Please try a different photo. 📸")
-        return None, seed, gr.update(visible=False)
-    
-    # Check if LoRA is selected
-    if lora_to_use is None:
-        gr.Warning("Please select an art style from the gallery first! 🎨")
-        return None, seed, gr.update(visible=False)
-    
-    # Add trigger word to prompt
-    trigger_word = lora_to_use.get("trigger_word", "")
-    
-    # Special handling for different art styles
-    if trigger_word == "ghibli":
-        prompt = f"Create a Studio Ghibli anime style portrait of the person in the photo, {prompt}. Maintain the facial identity while transforming into whimsical anime art style."
-    elif trigger_word == "homer":
-        prompt = f"Paint the person in Winslow Homer's American realist style, {prompt}. Keep facial features while applying watercolor and marine art techniques."
-    elif trigger_word == "gogh":
-        prompt = f"Transform the portrait into Van Gogh's post-impressionist style with swirling brushstrokes, {prompt}. Maintain facial identity with expressive colors."
-    elif trigger_word == "Cezanne":
-        prompt = f"Render the person in Paul Cézanne's geometric post-impressionist style, {prompt}. Keep facial structure while applying structured brushwork."
-    elif trigger_word == "Renoir":
-        prompt = f"Paint the portrait in Pierre-Auguste Renoir's impressionist style with soft light, {prompt}. Maintain identity with luminous skin tones."
-    elif trigger_word == "claude monet":
-        prompt = f"Create an impressionist portrait in Claude Monet's style with visible brushstrokes, {prompt}. Keep facial features while using light and color."
-    elif trigger_word == "fantasy":
-        prompt = f"Transform into an epic fantasy character portrait, {prompt}. Maintain facial identity while adding magical and fantastical elements."
-    elif trigger_word == ", How2Draw":
-        prompt = f"create a How2Draw sketch of the person of the photo {prompt}, maintain the facial identity of the person and general features"
-    elif trigger_word == ", video game screenshot in the style of THSMS":
-        prompt = f"create a video game screenshot in the style of THSMS with the person from the photo, {prompt}. maintain the facial identity of the person and general features"
-    else:
-        prompt = f"convert the style of this portrait photo to {trigger_word} while maintaining the identity of the person. {prompt}. Make sure to maintain the person's facial identity and features, while still changing the overall style to {trigger_word}."
+
+    # Use the prompt from the textbox directly.
+    final_prompt = prompt
+    print(f"Using prompt: {final_prompt}")
+
+    input_image = input_image.convert("RGB")
     
     try:
         image = pipe(
-            image=input_image, 
-            prompt=prompt,
+            image=input_image,
+            width=input_image.size[0],
+            height=input_image.size[1],
+            prompt=final_prompt,
             guidance_scale=guidance_scale,
-            generator=torch.Generator().manual_seed(seed),
+            num_inference_steps=num_inference_steps,
+            generator=torch.Generator().manual_seed(seed)
         ).images[0]
         
-        return image, seed, gr.update(visible=True)
+        # The seed value is no longer returned, as it's not being changed.
+        return image, lora_scale
     
     except Exception as e:
         print(f"Error during inference: {e}")
-        return None, seed, gr.update(visible=False)
+        # Return an error state for all outputs
+        return None, lora_scale
+
+# CSS styling
+css = """
+#main_app {
+    display: flex;
+    gap: 20px;
+}
+#box_column {
+    min-width: 400px;
+}
+#title{text-align: center}
+#title h1{font-size: 3em; display:inline-flex; align-items:center}
+#title img{width: 100px; margin-right: 0.5em}  
+#selected_lora {
+    color: #2563eb;
+    font-weight: bold;
+}
+#prompt {
+    flex-grow: 1;
+}
+#run_button {
+    background: linear-gradient(45deg, #2563eb, #3b82f6);
+    color: white;
+    border: none;
+    padding: 8px 16px;
+    border-radius: 6px;
+    font-weight: bold;
+}
+.custom_lora_card {
+    background: #f8fafc;
+    border: 1px solid #e2e8f0;
+    border-radius: 8px;
+    padding: 12px;
+    margin: 8px 0;
+}
+#gallery{
+    overflow: scroll !important
+}
+/* Custom CSS to ensure the input image is fully visible */
+#input_image_display div[data-testid="image"] img {
+    object-fit: contain !important;
+}
+"""
 
 # Create Gradio interface
-with gr.Blocks() as demo:
+with gr.Blocks(css=css, theme=gr.themes.Ocean(font=[gr.themes.GoogleFont("Lexend Deca"), "sans-serif"])) as demo:
     gr_flux_loras = gr.State(value=flux_loras_raw)
     
     title = gr.HTML(
-        """<h1>FLUX Kontex Super LoRAs🖖</h1>""",
+        """<h1><img src="https://huggingface.co/spaces/kontext-community/FLUX.1-Kontext-portrait/resolve/main/dora_kontext.png" alt="LoRA"> Kontext-Style LoRA Explorer</h1>""",
+        elem_id="title",
     )
+    gr.Markdown("A demo for the style LoRAs from the [Kontext-Style](https://huggingface.co/Kontext-Style) 🤗")
     
     selected_state = gr.State(value=None)
+    # The following states are no longer used by the simplified logic but kept for component structure
     custom_loaded_lora = gr.State(value=None)
+    lora_state = gr.State(value=1.0)
     
     with gr.Row(elem_id="main_app"):
         with gr.Column(scale=4, elem_id="box_column"):
             with gr.Group(elem_id="gallery_box"):
-                input_image = gr.Image(label="Upload your portrait photo 📸", type="pil", height=300)
-                
+                input_image = gr.Image(
+                    label="Upload a picture of yourself", 
+                    type="pil", 
+                    height=300,
+                    elem_id="input_image_display"
+                )
                 gallery = gr.Gallery(
-                    label="Choose Your Art Style",
+                    label="Pick a LoRA",
                     allow_preview=False,
-                    columns=3,
+                    columns=4,
                     elem_id="gallery",
                     show_share_button=False,
-                    height=400
+                    height=300,
+                    object_fit="contain"
                 )
                 
                 custom_model = gr.Textbox(
-                    label="🔗 Or use a custom LoRA from HuggingFace", 
+                    label="Or enter a custom HuggingFace FLUX LoRA", 
                     placeholder="e.g., username/lora-name",
-                    visible=True
+                    visible=False
                 )
                 custom_model_card = gr.HTML(visible=False)
-                custom_model_button = gr.Button("❌ Remove custom LoRA", visible=False)
+                custom_model_button = gr.Button("Remove custom LoRA", visible=False)
         
         with gr.Column(scale=5):
             with gr.Row():
                 prompt = gr.Textbox(
-                    label="Additional Details (optional)",
+                    label="Editing Prompt",
                     show_label=False,
                     lines=1,
                     max_lines=1,
-                    placeholder="Describe additional details, e.g., 'wearing a red hat' or 'smiling'",
+                    placeholder="opt - describe the person/subject, e.g. 'a man with glasses and a beard'",
                     elem_id="prompt"
                 )
-                run_button = gr.Button("Generate ✨", elem_id="run_button")
+                run_button = gr.Button("Generate", elem_id="run_button")
             
-            result = gr.Image(label="Your Artistic Portrait", interactive=False)
-            reuse_button = gr.Button("🔄 Reuse this image", visible=False)
+            result = gr.Image(label="Generated Image", interactive=False, height=512)
             
-            with gr.Accordion("⚙️ Advanced Settings", open=False):
+            with gr.Accordion("Advanced Settings", open=False):
                 lora_scale = gr.Slider(
-                    label="Style Strength",
+                    label="LoRA Scale",
                     minimum=0,
                     maximum=2,
                     step=0.1,
                     value=1.0,
-                    info="How strongly to apply the art style (1.0 = balanced)"
+                    info="Controls the strength of the LoRA effect"
                 )
                 seed = gr.Slider(
-                    label="Random Seed",
+                    label="Seed",
                     minimum=0,
                     maximum=MAX_SEED,
                     step=1,
                     value=0,
-                    info="Set to 0 for random results"
                 )
-                randomize_seed = gr.Checkbox(label="🎲 Randomize seed for each generation", value=True)
                 guidance_scale = gr.Slider(
-                    label="Image Guidance",
+                    label="Guidance Scale",
                     minimum=1,
                     maximum=10,
                     step=0.1,
                     value=2.5,
-                    info="How closely to follow the input image (lower = more creative)"
+                )
+                num_inference_steps = gr.Slider(
+                    label="Timesteps",
+                    minimum=1,
+                    maximum=100,
+                    step=1,
+                    value=28,
+                    info="Number of inference steps"
                 )
             
             prompt_title = gr.Markdown(
-                value="### 🎨 Select an art style from the gallery",
+                value="### Click on a LoRA in the gallery to select it",
                 visible=True,
                 elem_id="selected_lora",
             )
 
     # Event handlers
-    custom_model.input(
-        fn=load_custom_lora,
-        inputs=[custom_model],
-        outputs=[custom_model_card, custom_model_card, custom_model_button, custom_loaded_lora, gallery, prompt_title, selected_state],
-    )
-    
-    custom_model_button.click(
-        fn=remove_custom_lora,
-        outputs=[custom_model, custom_model_button, custom_model_card, custom_loaded_lora, selected_state]
-    )
+    # The custom model inputs are no longer needed as we've hidden them.
     
     gallery.select(
         fn=update_selection,
         inputs=[gr_flux_loras],
-        outputs=[prompt_title, prompt, selected_state],
+        outputs=[prompt_title, prompt, selected_state, lora_scale],
         show_progress=False
     )
     
     gr.on(
         triggers=[run_button.click, prompt.submit],
         fn=infer_with_lora_wrapper,
-        inputs=[input_image, prompt, selected_state, custom_loaded_lora, seed, randomize_seed, guidance_scale, lora_scale, gr_flux_loras],
-        outputs=[result, seed, reuse_button]
-    )
-    
-    reuse_button.click(
-        fn=lambda image: image,
-        inputs=[result],
-        outputs=[input_image]
+        inputs=[input_image, prompt, selected_state, lora_state, custom_loaded_lora, seed, guidance_scale, num_inference_steps, lora_scale, gr_flux_loras],
+        outputs=[result, lora_state]
     )
     
     # Initialize gallery
     demo.load(
-        fn=classify_gallery, 
+        fn=lambda loras: ([(item["image"], item["title"]) for item in loras], loras), 
         inputs=[gr_flux_loras], 
         outputs=[gallery, gr_flux_loras]
     )