LLM_Engineering_OLD/week2/community-contributions/AddingGeminiToDropdown.ipynb

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   "source": [
    "# Gemini to Dropdown"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d66eb067-7bae-4145-b613-6da2f40fbf27",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from typing import List\n",
    "from dotenv import load_dotenv\n",
    "from openai import OpenAI\n",
    "import google.generativeai as genai\n",
    "import anthropic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e36f8a93-8a65-48f2-bcad-7c47dd72ef3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import gradio as gr "
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "load_dotenv(override=True)\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "anthropic_api_key = os.getenv('ANTHROPIC_API_KEY')\n",
    "google_api_key = os.getenv('GOOGLE_API_KEY')\n",
    "\n",
    "if openai_api_key:\n",
    "    print(f\"OpenAI API Key exists and begins {openai_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"OpenAI API Key not set\")\n",
    "    \n",
    "if anthropic_api_key:\n",
    "    print(f\"Anthropic API Key exists and begins {anthropic_api_key[:7]}\")\n",
    "else:\n",
    "    print(\"Anthropic API Key not set\")\n",
    "\n",
    "if google_api_key:\n",
    "    print(f\"Google API Key exists and begins {google_api_key[:8]}\")\n",
    "else:\n",
    "    print(\"Google API Key not set\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "26d0099c-890f-4358-8c1d-7a708abcb105",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "openai = OpenAI()\n",
    "\n",
    "claude = anthropic.Anthropic()\n",
    "\n",
    "google.generativeai.configure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6606bfdb-964e-4d6f-b2a1-5017b99aa23d",
   "metadata": {},
   "outputs": [],
   "source": [
    "system_message = \"You are a helpful assistant\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0cfb96a-2dbe-4228-8efb-75947dbc3228",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_gpt(prompt):\n",
    "    messages = [\n",
    "        {\"role\": \"system\", \"content\": system_message},\n",
    "        {\"role\": \"user\", \"content\": prompt}\n",
    "      ]\n",
    "    stream = openai.chat.completions.create(\n",
    "        model='gpt-4o-mini',\n",
    "        messages=messages,\n",
    "        stream=True\n",
    "    )\n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        result += chunk.choices[0].delta.content or \"\"\n",
    "        yield result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9008a15d-0ee8-44e0-b123-225e7148113e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_claude(prompt):\n",
    "    result = claude.messages.stream(\n",
    "        model=\"claude-3-haiku-20240307\",\n",
    "        max_tokens=1000,\n",
    "        temperature=0.7,\n",
    "        system=system_message,\n",
    "        messages=[\n",
    "            {\"role\": \"user\", \"content\": prompt},\n",
    "        ],\n",
    "    )\n",
    "    response = \"\"\n",
    "    with result as stream:\n",
    "        for text in stream.text_stream:\n",
    "            response += text or \"\"\n",
    "            yield response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "378ad12e-6645-4647-807c-00995e360268",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_gemini(prompt):\n",
    "    gemini = genai.GenerativeModel(\n",
    "        model_name=\"gemini-2.0-flash\",\n",
    "        system_instruction=system_message\n",
    "    )\n",
    "    \n",
    "    stream = gemini.generate_content(prompt, stream=True)\n",
    "    \n",
    "    result = \"\"\n",
    "    for chunk in stream:\n",
    "        try:\n",
    "            part = chunk.text\n",
    "            if part:\n",
    "                result += part\n",
    "                yield result   \n",
    "        except Exception as e:\n",
    "            print(\"Chunk error:\", e)\n",
    "    \n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fd50e143-eead-49b1-8ea3-b440becd4bc9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def stream_model(prompt, model):\n",
    "    if model==\"GPT\":\n",
    "        result = stream_gpt(prompt)\n",
    "    elif model==\"Claude\":\n",
    "        result = stream_claude(prompt)\n",
    "    elif model==\"Gemini\":\n",
    "        result = stream_gemini(prompt)\n",
    "    else:\n",
    "        raise ValueError(\"Unknown model\")\n",
    "    yield from result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c7fc9cb4-fbb8-4301-86a6-96c90f67eb3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "view = gr.Interface(\n",
    "    fn=stream_model,\n",
    "    inputs=[gr.Textbox(label=\"Your message:\"), gr.Dropdown([\"GPT\", \"Claude\",\"Gemini\"], label=\"Select model\", value=\"GPT\")],\n",
    "    outputs=[gr.Markdown(label=\"Response:\")],\n",
    "    flagging_mode=\"never\"\n",
    ")\n",
    "view.launch()"
   ]
  }
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