Added support for model at remote address, see the mllm-streamlit project for this

app.py

@@ -26,10 +26,9 @@ lock            = False
 
 praise = 0
 
-print("Loading model...", end=" ")
-
 model_settings_path = "model.json"
 model_settings  = {
+    "remote_address": None,
     "model_path": None,
     "formatter": "chatml",
     "n_gpu_layers": -1,
@@ -59,7 +58,11 @@ if model_settings["model_path"] is None:
     raise Exception("No .gguf model was found in the program directory. Please specify a model's relative or absolute path using the generated model.json configuration file.")
 
 formatter   = importlib.import_module(model_settings["formatter"])
+LLM         = None
 
+# Enable loading the model only if the remote address is unspecified:
+if model_settings["remote_address"] is None:
+    print("Loading model...", end=" ")
 
     LLM = Llama(
         model_path      = model_settings["model_path"],
@@ -70,10 +73,36 @@ LLM = Llama(
 
     print("Loaded model {model_path}".format(model_path=model_settings["model_path"]))
 
+def get_response_remote(text):
+    global model_settings
+
+    remote_address = model_settings["remote_address"]
+
+    # e.g. http://127.0.0.1:11434/
+    # The project mllm-streamlit has a built-in webserver that runs inference on POSTed text:
+    response = requests.post(
+        remote_address,
+        data=text.encode("utf-8"),
+        headers={"Content-Type": "text/plain"},
+        stream=True)
+
+    response.raise_for_status()
+
+    for chunk in response.iter_content(chunk_size=None, decode_unicode=True):
+        if chunk:
+            chunk_text = json.loads(chunk)
+            print(chunk_text, end="")
+            yield chunk_text
+
+
 def get_response(text):
     global lock
     global model_settings
 
+    # If the remote address is specified, use this routine:
+    if model_settings["remote_address"] is not None:
+        return "".join(get_response_remote(text))
+
     while lock == True:
         time.sleep(0.1)
 
@@ -268,7 +297,6 @@ async def on_message(msg):
         print(f"{user_name}: {msg.content}")
         print(f"{bot_name}: ", end="")
 
-
         async with chl.typing():
             f_body = formatter.format(context, messages, for_completion=True)
             f_resp = await get_response_wrapper(f_body)