Update flux_train_ui.py

flux_train_ui.py

@@ -3,11 +3,6 @@ from huggingface_hub import whoami
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "1"
 import sys
 import spaces
-import gradio as gr
-from fastapi import FastAPI, Request
-import uvicorn
-from fastapi import Request
-
 # Add the current working directory to the Python path
 sys.path.insert(0, os.getcwd())
 
@@ -26,26 +21,6 @@ sys.path.insert(0, "ai-toolkit")
 from toolkit.job import get_job
 
 MAX_IMAGES = 150
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
 
 def load_captioning(uploaded_files, concept_sentence):
     uploaded_images = [file for file in uploaded_files if not file.endswith('.txt')]
@@ -452,35 +427,5 @@ with gr.Blocks(theme=theme, css=css) as demo:
 
     do_captioning.click(fn=run_captioning, inputs=[images, concept_sentence] + caption_list, outputs=caption_list)
 
-
-
-
-
-
-
-def train_model_ui(data: str):
-    print(f"🚀 Triggered with input: {data}")
-    return f"✅ Received: {data}"
-    
-
-
-# ⬇️ DO NOT remove this since you're keeping the manual launch
-# Main FastAPI app
-app = FastAPI()
-
-# Mount the Gradio app at root ("/")
-app = gr.mount_gradio_app(app, demo, path="/")
-
-# Custom POST endpoint
-@app.post("/trigger")
-async def trigger(request: Request):
-    body = await request.json()
-    input_data = body.get("input", "")
-    result = train_model_ui(input_data)
-    return {"result": result}
-
-# Run everything
 if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
-
-    
+    demo.launch(share=True, show_error=True)