Submission for Week 1 and Week 2 Exercises

week2/community-contributions/tochi/week_2_multimodel_technical_tutor.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "84ba5ab4",
+   "metadata": {},
+   "source": [
+    "# Week 2 Exercise: Build a Multimodal AI Technical Assistant\n",
+    "## Task Objective:\n",
+    "This week, you will upgrade the technical question-answering tool you built in Week 1 into a full-featured prototype. The goal is to apply all the concepts from Week 2 to create a more robust and interactive AI assistant.\n",
+    "\n",
+    "Core Requirements\n",
+    "Your prototype must include the following features:\n",
+    "\n",
+    "Gradio UI: Build a user-friendly interface for your assistant.\n",
+    "\n",
+    "Streaming Responses: Ensure the model's answers are streamed back to the user in real-time.\n",
+    "\n",
+    "System Prompt: Use a system prompt to define the AI's expertise and personality as a technical assistant.\n",
+    "\n",
+    "Model Switching: Implement a feature that allows the user to switch between at least two different models (e.g., an OpenAI model and Llama).\n",
+    "\n",
+    "Bonus: Demonstrate the use of a tool within your application for extra credit.\n",
+    " "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "6f83cbe7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import gradio as gr\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "18207ba0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Constants\n",
+    "MODEL_GPT = 'gpt-4o-mini'\n",
+    "MODEL_LLAMA = 'llama3.2'\n",
+    "OLLAMA_BASE_URL = \"http://localhost:11434/v1\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "27033ed9",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "API key found and looks good so far!\n"
+     ]
+    }
+   ],
+   "source": [
+    "# set up environment\n",
+    "load_dotenv(override=True)\n",
+    "api_key=os.getenv('OPENAI_API_KEY')\n",
+    "\n",
+    "if not api_key:\n",
+    "    print(\"No API key was found - please head over to the troubleshooting notebook in this folder to identify & fix!\")\n",
+    "elif not api_key.startswith(\"sk-proj-\"):\n",
+    "    print(\"An API key was found, but it doesn't start sk-proj-; please check you're using the right key - see troubleshooting notebook\")\n",
+    "elif api_key.strip() != api_key:\n",
+    "    print(\"An API key was found, but it looks like it might have space or tab characters at the start or end - please remove them - see troubleshooting notebook\")\n",
+    "else:\n",
+    "    print(\"API key found and looks good so far!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "b78ae89b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# initializing  models\n",
+    "\n",
+    "openai = OpenAI()\n",
+    "ollama = OpenAI(base_url=OLLAMA_BASE_URL, api_key='ollama')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "id": "1023bb5d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# here is the question; type over this to ask something new\n",
+    "system_prompt = \"\"\"\n",
+    "You are a technical assistant. You are to expect technical questions. You are to respond with detailed explanations to the technical question. You are to respond in Nigerian Pidgin English\n",
+    "\"\"\"\n",
+    "\n",
+    "question = \"\"\"\n",
+    "Please explain what tokens are in LLM\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "messages = [\n",
+    "    {\"role\": \"system\", \"content\": system_prompt},\n",
+    "    {\"role\": \"user\", \"content\": question},\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "id": "25ce65dc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def chat(message, history, model_choice):\n",
+    "    # history = [{\"role\": h[\"role\"], \"content\": h[\"content\"]} for h in history]\n",
+    "    messages = (\n",
+    "        [{\"role\": \"system\", \"content\": system_prompt}]\n",
+    "        + history\n",
+    "        + [{\"role\": \"user\", \"content\": message}]\n",
+    "    )\n",
+    "    if model_choice == \"openai\":\n",
+    "        try:\n",
+    "            stream = openai.chat.completions.create(\n",
+    "                model=MODEL_GPT, messages=messages, stream=True\n",
+    "            )\n",
+    "            \n",
+    "            response = \"\"\n",
+    "            for chunk in stream:\n",
+    "                response += chunk.choices[0].delta.content or \"\"\n",
+    "                yield response\n",
+    "        except Exception as e:\n",
+    "            yield f\"Error with OpenAI API: {e}\"\n",
+    "\n",
+    "    elif model_choice == \"llama\":\n",
+    "        try:\n",
+    "            stream = ollama.chat.completions.create(\n",
+    "                model=MODEL_LLAMA, messages=messages, stream=True\n",
+    "            )\n",
+    "            \n",
+    "            response = \"\"\n",
+    "            for chunk in stream:\n",
+    "                response += chunk.choices[0].delta.content or \"\"\n",
+    "                yield response\n",
+    "        except Exception as e:\n",
+    "            yield f\"Error with OpenAI API: {e}\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "8f46425c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model_selector = gr.Radio(\n",
+    "    [\"openai\", \"llama\"], \n",
+    "    label=\"Choose Model\", \n",
+    "    value=\"openai\" \n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "id": "d239c424",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "chatbot = gr.ChatInterface(fn=chat, type=\"messages\", additional_inputs=[model_selector])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "b00ac432",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "* Running on local URL:  http://127.0.0.1:7865\n",
+      "* To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7865/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "chatbot.launch()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8ad21f62",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
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+  "kernelspec": {
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+   "language": "python",
+   "name": "python3"
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