Update app.py

app.py

@@ -1,75 +1,136 @@
 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
 
 css_code = os.getenv("DazDinGo_CSS")
 
-def load_fn(models):
-    global models_load
-    models_load = {}
-    for model in models:
-        if model not in models_load.keys():
-            try:
-                m = gr.load(f'models/{model}')
-            except Exception as error:
-                m = gr.Interface(lambda txt: None, ['text'], ['image'])
-            models_load.update({model: m})
+# 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'])
 
-load_fn(models)
+app = FastAPI()
 
-num_models = len(models)
-default_models = models[:num_models]
-
-def extend_choices(choices):
-    return choices + (num_models - len(choices)) * ['NA']
-
-def update_imgbox(choices):
-    choices_plus = extend_choices(choices)
-    return [gr.Image(None, label=m, visible=(m != 'NA'), elem_id="custom_image") for m in choices_plus]
-
-def gen_fn(model_str, prompt):
+def gen_image(model_str, prompt):
     if model_str == 'NA':
         return None
     noise = str(randint(0, 4294967296))
     klir = '| ultra detail, ultra elaboration, ultra quality, perfect'
     return models_load[model_str](f'{prompt} {klir} {noise}')
 
+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"}
+    
+    image = gen_image(model, prompt)
+    if image is None:
+        return {"error": "Image generation failed"}
+    
+    base64_str = image_to_base64(image)
+    
+    return {
+        "status": "success",
+        "model": model,
+        "prompt": 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", height=250)
+            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 images', elem_id="custom_gen_button")
-            stop_button = gr.Button('Stop', variant='secondary', interactive=False, elem_id="custom_stop_button")
+            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 images', elem_id="custom_gen_button"), gr.Button('Stop', variant='secondary', interactive=True, elem_id="custom_stop_button")
+                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 images', elem_id="custom_gen_button"), gr.Button('Stop', variant='secondary', interactive=False, elem_id="custom_stop_button")
+                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():
-        output = [gr.Image(label=m, width=512, max_height=768, elem_id="custom_image", show_label=True, interactive=False, show_share_button=False) for m in default_models]
-        current_models = [gr.Textbox(m, visible=False) for m in default_models]
-        for m, o in zip(current_models, output):
-            gen_event = gen_button.click(gen_fn, [m, txt_input], o)
-            stop_button.click(on_stop_click, inputs=None, outputs=[gen_button, stop_button], cancels=[gen_event])
-    
-    with gr.Accordion('Model selection', elem_id="custom_accordion"):
-        model_choice = gr.CheckboxGroup(models, label=f'{num_models} different models selected', value=default_models, interactive=True, elem_id="custom_checkbox_group")
-        model_choice.change(update_imgbox, model_choice, output)
-        model_choice.change(extend_choices, model_choice, current_models)
-    
-    with gr.Row():
-        gr.HTML("")
+        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):
+                image = gen_image(model_str, prompt)
+                if image is None:
+                    return None, {"error": "Generation failed"}
+                
+                base64_str = image_to_base64(image)
+                response = {
+                    "status": "success",
+                    "model": model_str,
+                    "prompt": 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(concurrency_count=50)
-demo.launch()
+
+# 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)