Update app.py

app.py

@@ -1,6 +1,10 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
+from fastapi import FastAPI, Request
+from fastapi.responses import JSONResponse
 import json
+import univcron
+from huggingface_hub import InferenceClient
+
+app = FastAPI()
 
 # Inisialisasi HuggingFace client
 client = InferenceClient("meta-llama/Meta-Llama-3-8B-Instruct")
@@ -44,27 +48,35 @@ def process_json(json_input):
     chat_history = chat_llama(chat_history)
     return json.dumps(chat_history, indent=2), ""
 
-# Definisikan antarmuka Gradio
-def gradio_interface():
-    with gr.Blocks() as demo:
-        with gr.Row():
-            with gr.Column():
-                chatbot = gr.Chatbot()
-                msg = gr.Textbox(interactive=True)
-                with gr.Row():
-                    clear = gr.ClearButton([msg, chatbot])
-                    send_btn = gr.Button("Send", variant='primary')
-                msg.submit(fn=chat_mem, inputs=[msg, chatbot], outputs=[msg, chatbot])
-                send_btn.click(fn=chat_mem, inputs=[msg, chatbot], outputs=[msg, chatbot])
-            
-            with gr.Column():
-                json_input = gr.Textbox(placeholder='Input JSON here...', interactive=True, lines=10)
-                json_output = gr.Textbox(label='Output JSON', interactive=False, lines=10)
-                process_btn = gr.Button("Process JSON", variant='primary')
-                process_btn.click(fn=process_json, inputs=json_input, outputs=[json_output])
+@app.post("/chat_llama")
+async def chat_llama_endpoint(request: Request):
+    data = await request.json()
+    chat_history = data.get('chat_history', [])
+    response = chat_llama(chat_history)
+    return JSONResponse(content=response)
+
+@app.post("/chat_mem")
+async def chat_mem_endpoint(request: Request):
+    data = await request.json()
+    message = data.get('message', '')
+    chat_history = data.get('chat_history', [])
+    response = chat_mem(message, chat_history)
+    return JSONResponse(content=response)
+
+@app.post("/process_json")
+async def process_json_endpoint(request: Request):
+    data = await request.json()
+    json_input = data.get('json_input', '')
+    response = process_json(json_input)
+    return JSONResponse(content=response)
+
+def schedule_task():
+    print("Scheduled task is running")
 
-    return demo
+# Tambahkan tugas terjadwal menggunakan univcron
+univcron.every(10).seconds.do(schedule_task)
 
-# Jalankan antarmuka Gradio
-demo = gradio_interface()
-demo.launch(server_name="0.0.0.0", server_port=7860, share=False)
+if __name__ == "__main__":
+    import uvicorn
+    # Jalankan server FastAPI
+    uvicorn.run(app, host="0.0.0.0", port=7860)