Implement Automatic Model List Retrieval for SiliconCloud LLM API Node

.prettierrc

@@ -0,0 +1,19 @@
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+}

js/siliconcloud_llm_api.js

@@ -2,7 +2,7 @@ import { app } from "../../scripts/app.js";
 import { ComfyWidgets } from "../../scripts/widgets.js";
 
 app.registerExtension({
-    name: "bizyair.siliconcloud.llm.api",
+    name: "bizyair.siliconcloud.llm.api.populate",
     async beforeRegisterNodeDef(nodeType, nodeData, app) {
         if (nodeData.name === "BizyAirSiliconCloudLLMAPI") {
             function populate(text) {
@@ -43,4 +43,72 @@ app.registerExtension({
             };
         }
     },
-})
+});
+
+app.registerExtension({
+    name: "bizyair.siliconcloud.llm.api.model_fetch",
+    async beforeRegisterNodeDef(nodeType, nodeData, app) {
+        if (nodeData.name === "BizyAirSiliconCloudLLMAPI") {
+            const originalNodeCreated = nodeType.prototype.onNodeCreated;
+            nodeType.prototype.onNodeCreated = async function () {
+                if (originalNodeCreated) {
+                    originalNodeCreated.apply(this, arguments);
+                }
+
+                const modelWidget = this.widgets.find((w) => w.name === "model");
+
+                const fetchModels = async () => {
+                    try {
+                        const response = await fetch("/bizyair/get_silicon_cloud_models", {
+                            method: "POST",
+                            headers: {
+                                "Content-Type": "application/json",
+                            },
+                            body: JSON.stringify({}),
+                        });
+
+                        if (response.ok) {
+                            const models = await response.json();
+                            console.debug("Fetched models:", models);
+                            return models;
+                        } else {
+                            console.error(`Failed to fetch models: ${response.status}`);
+                            return [];
+                        }
+                    } catch (error) {
+                        console.error(`Error fetching models`, error);
+                        return [];
+                    }
+                };
+
+                const updateModels = async () => {
+                    const prevValue = modelWidget.value;
+                    modelWidget.value = "";
+                    modelWidget.options.values = [];
+
+                    const models = await fetchModels();
+
+                    modelWidget.options.values = models;
+                    console.debug("Updated modelWidget.options.values:", modelWidget.options.values);
+
+                    if (models.includes(prevValue)) {
+                        modelWidget.value = prevValue; // stay on current.
+                    } else if (models.length > 0) {
+                        modelWidget.value = models[0]; // set first as default.
+                    }
+
+                    console.debug("Updated modelWidget.value:", modelWidget.value);
+                    app.graph.setDirtyCanvas(true);
+                };
+
+                const dummy = async () => {
+                    // calling async method will update the widgets with actual value from the browser and not the default from Node definition.
+                };
+
+                // Initial update
+                await dummy(); // this will cause the widgets to obtain the actual value from web page.
+                await updateModels();
+            };
+        }
+    },
+});

llm.py

@@ -1,6 +1,10 @@
 import json
 import os
 
+import requests
+from aiohttp import web
+from server import PromptServer
+
 from bizyair.common.env_var import BIZYAIR_SERVER_ADDRESS
 from bizyair.image_utils import decode_data, encode_data
 
@@ -13,32 +17,38 @@
 )
 
 
-class SiliconCloudLLMAPI:
+@PromptServer.instance.routes.post("/bizyair/get_silicon_cloud_models")
+async def get_silicon_cloud_models_endpoint(request):
+    data = await request.json()
+    api_key = data.get("api_key", get_api_key())
+    url = "https://api.siliconflow.cn/v1/models"
+    headers = {"accept": "application/json", "authorization": f"Bearer {api_key}"}
+    try:
+        response = requests.get(url, headers=headers)
+        response.raise_for_status()
+        data = response.json()
+        models = [model["id"] for model in data["data"]]
+        models.append("No LLM Enhancement")
+        return web.json_response(models)
+    except requests.RequestException as e:
+        print(f"Error fetching models: {e}")
+        return web.json_response(["Error fetching models"], status=500)
 
-    display_name_to_id = {
-        "Yi1.5 9B": "01-ai/Yi-1.5-9B-Chat-16K",
-        "DeepSeekV2 Chat": "deepseek-ai/DeepSeek-V2-Chat",
-        "(Free)GLM4 9B Chat": "THUDM/glm-4-9b-chat",
-        "Qwen2 72B Instruct": "Qwen/Qwen2-72B-Instruct",
-        "(Free)Qwen2 7B Instruct": "Qwen/Qwen2-7B-Instruct",
-        "No LLM Enhancement": "Bypass",
-    }
+
+class SiliconCloudLLMAPI:
+    def __init__(self):
+        pass
 
     @classmethod
     def INPUT_TYPES(s):
-        models = list(s.display_name_to_id.keys())
-        default_sysmtem_prompt = """你是一个 stable diffusion prompt 专家，为我生成适用于 Stable Diffusion 模型的prompt。
-我给你相关的单词，你帮我扩写为适合 Stable Diffusion 文生图的 prompt。要求：
-1. 英文输出
-2. 除了 prompt 外，不要输出任何其它的信息
-"""
+        default_system_prompt = """你是一个 stable diffusion prompt 专家，为我生成适用于 Stable Diffusion 模型的prompt。 我给你相关的单词，你帮我扩写为适合 Stable Diffusion 文生图的 prompt。要求： 1. 英文输出 2. 除了 prompt 外，不要输出任何其它的信息 """
         return {
             "required": {
-                "model": (models, {"default": "(Free)GLM4 9B Chat"}),
+                "model": ((), {}),
                 "system_prompt": (
                     "STRING",
                     {
-                        "default": default_sysmtem_prompt,
+                        "default": default_system_prompt,
                         "multiline": True,
                         "dynamicPrompts": True,
                     },
@@ -68,10 +78,10 @@ def INPUT_TYPES(s):
     def get_llm_model_response(
         self, model, system_prompt, user_prompt, max_tokens, temperature
     ):
-        if self.display_name_to_id[model] == "Bypass":
+        if model == "No LLM Enhancement":
             return {"ui": {"text": (user_prompt,)}, "result": (user_prompt,)}
         response = get_llm_response(
-            self.display_name_to_id[model],
+            model,
             system_prompt,
             user_prompt,
             max_tokens,