Update app.py

app.py

@@ -1,25 +1,20 @@
-import gradio as gr
-import websockets
 import asyncio
+import websockets
 
-async def send_to_websocket(message):
-    uri = "wss://clone3-imagex-clone-advance.hf.space/queue/join"
+async def hello():
+    uri = "ws://localhost:8765"
     async with websockets.connect(uri) as websocket:
-        await websocket.send(message)
-        response = await websocket.recv()
-        return response
-
-def predict(input_text):
-    # You can replace this with your actual machine learning model prediction logic
-    # For now, we'll just send the input to the WebSocket server and echo the response
-    response = asyncio.run(send_to_websocket(input_text))
-    return response
-
-iface = gr.Interface(
-    fn=predict,
-    inputs=gr.Textbox(),
-    outputs="text",
-    live=True,
-)
+        #messages = ["{"data":["spider",1683852825,8,4,true],"event_data":null,"fn_index":2,"session_hash":"kb46puzhzr"}", "Second message"]
+        messages = [
+                    '{"fn_index":2,"session_hash":"kb46puzhzr"}',
+                    '{"data":["spider",1683852825,8,4,true],"event_data":null,"fn_index":2,"session_hash":"kb46puzhzr"}'
+                    ]
+        for message in messages:
+            print(f"Sending: {message}")
+            await websocket.send(message)
+            
+            response = await websocket.recv()
+            print(f"Received: {response}")
 
-iface.launch()
+if __name__ == "__main__":
+    asyncio.run(hello())