Add application file2

app.py

@@ -1,19 +1,21 @@
-import os
+import spaces
 import gradio as gr
 import torch
 from PIL import Image, PngImagePlugin
 from diffusers import DiffusionPipeline
 import random
+import os
+import pygsheets
 from datetime import datetime
 import json
 from gradio_client import Client as client_gradio
 from supabase import create_client, Client
-from huggingface_hub import login
 
-# Inicializar Supabase com variáveis de ambiente
-SUPABASE_URL = os.getenv("SUPABASE_URL")
-SUPABASE_KEY = os.getenv("SUPABASE_KEY")
-supabase: Client = create_client(SUPABASE_URL, SUPABASE_KEY)
+
+# Initialize supabase 
+url: str = os.getenv('SUPABASE_URL')
+key: str = os.getenv('SUPABASE_KEY')
+supabase: Client = create_client(url, key)
 
 # Obter o token do Hugging Face a partir dos secrets
 hf_token = os.getenv("HF_TOKEN")
@@ -21,43 +23,47 @@ hf_token = os.getenv("HF_TOKEN")
 # Autenticar com o Hugging Face
 login(token=hf_token)
 
-# Carregar modelo base e LoRA
+# Initialize the base model and specific LoRA
 base_model = "black-forest-labs/FLUX.1-dev"
-pipe = DiffusionPipeline.from_pretrained(
-    base_model,
-    torch_dtype=torch.bfloat16,
-    use_auth_token=True
-)
+pipe = DiffusionPipeline.from_pretrained(base_model, torch_dtype=torch.bfloat16)
 
 lora_repo = "markury/AndroFlux"
-trigger_word = ""
-pipe.load_lora_weights(lora_repo, weight_name="AndroFlux-v19.safetensors")
+trigger_word = ""  # Leave trigger_word blank if not used.
+pipe.load_lora_weights(lora_repo, weight_name = "AndroFlux-v19.safetensors")
 
 pipe.to("cuda")
 
 MAX_SEED = 2**32-1
 
-
-def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale):
+@spaces.GPU(duration=80)
+def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, progress=gr.Progress(track_tqdm=True)):
+    # Set random seed for reproducibility
     if randomize_seed:
         seed = random.randint(0, MAX_SEED)
     generator = torch.Generator(device="cuda").manual_seed(seed)
 
-    # Moderação de prompt
+    #Moderation 
+
     moderation_client = client_gradio("duchaba/Friendly_Text_Moderation")
     result = moderation_client.predict(
-        msg=f"{prompt}", safer=0.02, api_name="/fetch_toxicity_level"
+		msg=f"{prompt}",
+		safer=0.02,
+		api_name="/fetch_toxicity_level"
     )
-    
-    if float(json.loads(result[1])["sexual_minors"]) > 0.03:
-        supabase.table("requests").insert({
-            "prompt": prompt, "cfg_scale": cfg_scale, "steps": steps,
-            "randomized_seed": randomize_seed, "seed": seed,
-            "lora_scale": lora_scale, "moderated": True
-        }).execute()
+
+    if float(json.loads(result[1])['sexual_minors']) > 0.03 : 
+        print('Minors')
+        response_data = (supabase.table("requests")
+        .insert({"prompt":prompt, "cfg_scale":cfg_scale, "steps":steps, "randomized_seed": randomize_seed, "seed":seed, "lora_scale" : lora_scale, "moderated" : 'true'})
+        .execute()
+                        )
         raise gr.Error("Unauthorized request 💥!")
 
-    # Gerar imagem
+    # Update progress bar (0% saat mulai)
+    progress(0, "Starting image generation...")
+
+
+    # Generate image using the pipeline
     image = pipe(
         prompt=f"{prompt} {trigger_word}",
         num_inference_steps=steps,
@@ -69,55 +75,81 @@ def run_lora(prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora
         max_sequence_length=512
     ).images[0]
 
-    # Salvar imagem temporária
+    # Save the image to a file with a unique name in /tmp directory
     timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
     image_filename = f"generated_image_{timestamp}.png"
     image_path = os.path.join("/tmp/gradio", image_filename)
 
-    # Adicionar metadata
+    # Add Metadata
+    new_metadata_string = f"{prompt}\nNegative prompt:  none \nSteps: {steps}, CFG scale: {cfg_scale}, Seed: {seed}, Lora hashes: AndroFlux-v19: c44afd41ece1"
     metadata = PngImagePlugin.PngInfo()
-    metadata.add_text("parameters", f"{prompt}\nSteps: {steps}, CFG: {cfg_scale}, Seed: {seed}")
+    metadata.add_text("parameters", new_metadata_string)
+
+    #Save the tmp image
     image.save(image_path, pnginfo=metadata)
 
-    # Salvar no Supabase (se permitido)
+    
+
+
+    #Log queries
     try:
         if "girl" not in prompt and "woman" not in prompt:
-            response = supabase.storage.from_('generated_images').upload(
-                image_filename, image_path, file_options={"content-type": "image/png;charset=UTF-8"}
+            #Save image in supabase
+            response = supabase.storage.from_('generated_images').upload(image_filename, image_path,file_options={"content-type":"image/png;charset=UTF-8"})
+            print(response.dict)
+            #Log request in supabase
+            response_data = (supabase.table("requests")
+            .insert({"prompt":prompt, "cfg_scale":cfg_scale, "steps":steps, "randomized_seed": randomize_seed, "seed":seed, "lora_scale" : lora_scale, "image_url" : response.full_path})
+            .execute()
             )
-            image_url = response.full_path
-            supabase.table("requests").insert({
-                "prompt": prompt, "cfg_scale": cfg_scale, "steps": steps,
-                "randomized_seed": randomize_seed, "seed": seed,
-                "lora_scale": lora_scale, "image_url": image_url
-            }).execute()
-    except Exception as error:
-        print("Erro ao salvar no Supabase:", error)
-
-    return image, seed
 
-# Interface Gradio
-with gr.Blocks() as app:
+    except Exception as error:
+        # handle the exception
+        print("An exception occurred:", error)
+
+    yield image, seed
+
+# Example cached image and settings
+example_image_path = "blond_5.webp"  # Replace with the actual path to the example image
+example_prompt = """a full frontal view photo of a athletic man with olive skin in his late twenties standing on a flowery terrace at golden hour. He is fully naked with a thick uncut penis and blond pubic hair. The man has long blond hair and has a dominant expression. The setting is outdoors, with a peaceful and aesthetic atmosphere."""
+example_cfg_scale = 3.5
+example_steps = 25
+example_width = 896
+example_height = 1152
+example_seed = 556215326
+example_lora_scale = 1
+
+def load_example():
+    # Load example image from file
+    example_image = Image.open(example_image_path)
+    return example_prompt, example_cfg_scale, example_steps, True, example_seed, example_width, example_height, example_lora_scale, example_image
+
+gr_theme = os.getenv("THEME")
+with gr.Blocks(theme=gr_theme) as app:
     gr.Markdown("# Androflux Image Generator")
     with gr.Row():
         with gr.Column(scale=3):
-            prompt = gr.TextArea(label="Prompt", placeholder="Escreva um prompt", lines=3)
-            generate_button = gr.Button("Gerar")
-            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=7.5)
-            steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=25)
-            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=896)
-            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=1152)
+            prompt = gr.TextArea(label="Prompt", placeholder="Type a prompt of max 77 characters", lines=3)
+            generate_button = gr.Button("Generate")
+            cfg_scale = gr.Slider(label="CFG Scale", minimum=1, maximum=20, step=0.5, value=example_cfg_scale)
+            steps = gr.Slider(label="Steps", minimum=1, maximum=100, step=1, value=example_steps)
+            width = gr.Slider(label="Width", minimum=256, maximum=1536, step=64, value=example_width)
+            height = gr.Slider(label="Height", minimum=256, maximum=1536, step=64, value=example_height)
             randomize_seed = gr.Checkbox(False, label="Randomize seed")
-            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=556215326)
-            lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=1)
+            seed = gr.Slider(label="Seed", minimum=0, maximum=MAX_SEED, step=1, value=example_seed)
+            lora_scale = gr.Slider(label="LoRA Scale", minimum=0, maximum=1, step=0.01, value=example_lora_scale)
         with gr.Column(scale=1):
             result = gr.Image(label="Generated Image")
+            gr.Markdown("Generate images using Androflux Lora and a text prompt.\n[[non-commercial license, Flux.1 Dev](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md)]")
 
+    # Automatically load example data and image when the interface is launched
+    app.load(load_example, inputs=[], outputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale, result])
+    
     generate_button.click(
         run_lora,
         inputs=[prompt, cfg_scale, steps, randomize_seed, seed, width, height, lora_scale],
-        outputs=[result, seed]
+        outputs=[result, seed],
     )
 
 app.queue()
-app.launch()
+app.launch()