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all_models.py

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+models = [
+'burhansyam/davina',
+'burhansyam/bulsut',
+'burhansyam/cipa',
+'burhansyam/difarina',
+'burhansyam/lalawidy',
+'burhansyam/nafa',
+'burhansyam/catheez',
+'burhansyam/eca',
+'burhansyam/vior',
+'burhansyam/gaby',
+'burhansyam/livy',
+'burhansyam/rachel',
+'burhansyam/kame',
+'burhansyam/inara',
+'burhansyam/ranty',
+'burhansyam/novisasmita',
+'burhansyam/dara',
+'burhansyam/shinta',
+'burhansyam/arlida',    
+'burhansyam/pamela',
+'burhansyam/berliana',
+'burhansyam/fbyana',    
+'burhansyam/wulan',
+'burhansyam/lunmay',
+'burhansyam/gisel',
+'burhansyam/pevita',
+'burhansyam/ariel',
+'burhansyam/mahal',
+'burhansyam/sherina',
+'burhansyam/sandrina',
+'burhansyam/nagita',
+'burhansyam/wika',
+'burhansyam/angel',
+'burhansyam/sarah',
+'burhansyam/cupi',
+'burhansyam/gita',
+'burhansyam/ayuting',
+'burhansyam/zoe',
+'burhansyam/taniayu',
+'burhansyam/3gp',
+'burhansyam/djbidy',
+'burhansyam/busalsa',
+'burhansyam/fuji',
+'burhansyam/msbrew',
+'burhansyam/vandal',
+'burhansyam/marco',
+'burhansyam/logo',
+'burhansyam/bowo',  
+'burhansyam/cristiano',      
+'burhansyam/bikini',
+'burhansyam/nyemot',   
+'burhansyam/minitoy',
+'burhansyam/micro',
+'burhansyam/naturelens',
+'burhansyam/ghibli',
+'burhansyam/buncit',
+'burhansyam/doggy',
+'burhansyam/uncen'
+]

app.py

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+import os
+import gradio as gr
+import base64
+from random import randint
+from all_models import models
+from io import BytesIO
+from PIL import Image
+from fastapi import FastAPI, Request
+from deep_translator import GoogleTranslator
+
+css_code = os.getenv("DazDinGo_CSS")
+
+# Initialize translator
+translator = GoogleTranslator(source='auto', target='en')
+
+# Load models
+models_load = {}
+for model in models:
+    try:
+        models_load[model] = gr.load(f'models/{model}')
+    except Exception as error:
+        models_load[model] = gr.Interface(lambda txt: None, ['text'], ['image'])
+
+app = FastAPI()
+
+def gen_image(model_str, prompt):
+    if model_str == 'NA':
+        return None
+    # Translate prompt to English {noise} {klir}
+    translated_prompt = translator.translate(prompt)
+    noise = str(randint(0, 4294967296))
+    klir = '| ultra detail, ultra elaboration, ultra quality, perfect'
+    return models_load[model_str](f'{translated_prompt} {noise} {klir}')
+
+def image_to_base64(image):
+    buffered = BytesIO()
+    if isinstance(image, str):  # if it's a file path
+        img = Image.open(image)
+        img.save(buffered, format="JPEG")
+    else:  # if it's a PIL Image
+        image.save(buffered, format="JPEG")
+    return base64.b64encode(buffered.getvalue()).decode()
+
+# API endpoint
+@app.post("/generate")
+async def api_generate(request: Request):
+    data = await request.json()
+    model = data.get('model', models[0])
+    prompt = data.get('prompt', '')
+    
+    if model not in models:
+        return {"error": "Model not found"}
+    
+    # Translate prompt to English for API endpoint too
+    translated_prompt = translator.translate(prompt)
+    image = gen_image(model, translated_prompt)
+    if image is None:
+        return {"error": "Image generation failed"}
+    
+    base64_str = image_to_base64(image)
+    
+    return {
+        "status": "success",
+        "model": model,
+        "original_prompt": prompt,
+        "translated_prompt": translated_prompt,
+        "image_base64": base64_str,
+        "image_format": "jpeg"
+    }
+
+# Gradio Interface
+def make_me():
+    with gr.Row():
+        with gr.Column(scale=4):
+            txt_input = gr.Textbox(
+                label='Your prompt:', 
+                lines=4, 
+                container=False, 
+                elem_id="custom_textbox", 
+                placeholder="Prompt"
+            )
+        
+        with gr.Column(scale=1):
+            gen_button = gr.Button('Generate image', elem_id="custom_gen_button")
+            stop_button = gr.Button('Stop', variant='secondary', interactive=False, 
+                                elem_id="custom_stop_button")
+            
+            def on_generate_click():
+                return gr.Button('Generate image', elem_id="custom_gen_button"), gr.Button('Stop', variant='secondary', interactive=True, elem_id="custom_stop_button")
+            
+            def on_stop_click():
+                return gr.Button('Generate image', elem_id="custom_gen_button"), gr.Button('Stop', variant='secondary', interactive=False, elem_id="custom_stop_button")
+            
+            gen_button.click(on_generate_click, inputs=None, outputs=[gen_button, stop_button])
+            stop_button.click(on_stop_click, inputs=None, outputs=[gen_button, stop_button])
+    
+    with gr.Row():
+        with gr.Column():
+            model_dropdown = gr.Dropdown(models, label="Select Model", 
+                                      value=models[0] if models else None)
+            output_image = gr.Image(
+                label="Generated Image", 
+                width=512,
+                height=768,
+                elem_id="custom_image", 
+                show_label=True, 
+                interactive=False
+            )
+            
+            # JSON output
+            json_output = gr.JSON(label="API Response")
+            
+            def generate_wrapper(model_str, prompt):
+                # Translate prompt to English
+                translated_prompt = translator.translate(prompt)
+                image = gen_image(model_str, translated_prompt)
+                if image is None:
+                    return None, {"error": "Generation failed"}
+                
+                base64_str = image_to_base64(image)
+                response = {
+                    "status": "success",
+                    "model": model_str,
+                    "original_prompt": prompt,
+                    "translated_prompt": translated_prompt,
+                    "image_base64": base64_str,
+                    "image_format": "jpeg"
+                }
+                return image, response
+            
+            gen_event = gen_button.click(generate_wrapper, [model_dropdown, txt_input], 
+                                     [output_image, json_output])
+            stop_button.click(on_stop_click, inputs=None, 
+                            outputs=[gen_button, stop_button], cancels=[gen_event])
+
+# Create Gradio app
+with gr.Blocks(css=css_code) as demo: 
+    make_me()
+
+# Enable queue before mounting
+demo.queue()
+
+# Mount Gradio app to FastAPI
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

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+wheel
+fastapi>=0.109.1
+gradio>=4.19.1
+Pillow>=10.1.0
+deep-translator>=1.11.4
+uvicorn>=0.27.0
+numpy