add code for langgraph llm using gradio client

gradio_client.ipynb

@@ -0,0 +1,159 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e7319ea5",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from typing import List, Optional\n",
+    "from langchain.callbacks.manager import CallbackManagerForLLMRun\n",
+    "from pydantic import Field\n",
+    "from gradio_client import Client, handle_file\n",
+    "\n",
+    "# Use local BaseChatModel if available, else fallback to langchain_core\n",
+    "try:\n",
+    "    from BaseChatModel import BaseChatModel\n",
+    "except ImportError:\n",
+    "    from langchain_core.language_models.chat_models import BaseChatModel\n",
+    "\n",
+    "try:\n",
+    "    from langchain_core.messages.base import BaseMessage\n",
+    "except ImportError:\n",
+    "    from langchain.schema import BaseMessage\n",
+    "\n",
+    "try:\n",
+    "    from langchain_core.messages import AIMessage\n",
+    "except ImportError:\n",
+    "    from langchain.schema import AIMessage\n",
+    "\n",
+    "try:\n",
+    "    from langchain_core.outputs import ChatResult\n",
+    "except ImportError:\n",
+    "    from langchain.schema import ChatResult\n",
+    "\n",
+    "try:\n",
+    "    from langchain_core.outputs import ChatGeneration\n",
+    "except ImportError:\n",
+    "    from langchain.schema import ChatGeneration\n",
+    "\n",
+    "\n",
+    "class GradioChatModel(BaseChatModel):\n",
+    "    client: Client = Field(default=None, description=\"Gradio client for API communication\")\n",
+    "\n",
+    "    def __init__(self, client: Client = None, **kwargs):\n",
+    "        super().__init__(**kwargs)\n",
+    "        if client is None:\n",
+    "            client = Client(\"apjanco/fantastic-futures\")\n",
+    "        object.__setattr__(self, 'client', client)\n",
+    "\n",
+    "    @property\n",
+    "    def _llm_type(self) -> str:\n",
+    "        return \"gradio_chat_model\"\n",
+    "\n",
+    "    def _generate(\n",
+    "        self,\n",
+    "        messages: List[BaseMessage],\n",
+    "        stop: Optional[List[str]] = None,\n",
+    "        run_manager: Optional[CallbackManagerForLLMRun] = None,\n",
+    "    ) -> ChatResult:\n",
+    "        # Use the first message as prompt, and optionally extract image url if present\n",
+    "        prompt = None\n",
+    "        image_url = None\n",
+    "        for msg in messages:\n",
+    "            if hasattr(msg, \"content\") and msg.content:\n",
+    "                if prompt is None:\n",
+    "                    prompt = msg.content\n",
+    "                # Optionally, look for an image url in the message metadata or content\n",
+    "                if hasattr(msg, \"image\") and msg.image:\n",
+    "                    image_url = msg.image\n",
+    "        if prompt is None:\n",
+    "            prompt = \"Hello!!\"\n",
+    "        if image_url is None:\n",
+    "            # fallback image\n",
+    "            image_url = 'https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png'\n",
+    "\n",
+    "        image_file = handle_file(image_url)\n",
+    "        response = self.client.predict(\n",
+    "            image=image_file,\n",
+    "            model_id='nanonets/Nanonets-OCR-s',\n",
+    "            prompt=prompt,\n",
+    "            api_name=\"/run_example\"\n",
+    "        )\n",
+    "        # The response may be a string or dict; wrap as AIMessage\n",
+    "        if isinstance(response, dict) and \"message\" in response:\n",
+    "            content = response[\"message\"]\n",
+    "        else:\n",
+    "            content = str(response)\n",
+    "        message = AIMessage(content=content)\n",
+    "        # Wrap the AIMessage in a ChatGeneration object\n",
+    "        chat_generation = ChatGeneration(message=message)\n",
+    "        return ChatResult(generations=[chat_generation])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "e9a50bd3",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded as API: https://apjanco-fantastic-futures.hf.space ✔\n",
+      "content='This is an icon of a bus.'\n"
+     ]
+    }
+   ],
+   "source": [
+    "from langchain.schema import HumanMessage\n",
+    "\n",
+    "# Create a HumanMessage with content and image attribute\n",
+    "class HumanMessageWithImage(HumanMessage):\n",
+    "    def __init__(self, content, image=None, **kwargs):\n",
+    "        super().__init__(content=content, **kwargs)\n",
+    "        self.image = image\n",
+    "\n",
+    "custom_llm = GradioChatModel()\n",
+    "image_url = \"https://raw.githubusercontent.com/gradio-app/gradio/main/test/test_files/bus.png\"\n",
+    "prompt = \"what is this?\"\n",
+    "msg = HumanMessageWithImage(content=prompt, image=image_url)\n",
+    "\n",
+    "# Call invoke with a list of messages\n",
+    "result = custom_llm.invoke([msg])\n",
+    "print(result)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "920ba4af",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}