Merge pull request #809 from kbaah/kwabena_bootcamp

Kwabena Bootcamp

week2/community-contributions/kwabena/week2_solution_.ipynb

@@ -0,0 +1,173 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fd1cdd6e",
+   "metadata": {},
+   "source": [
+    "## Week 2 - Full Prototype for Technical Questions Answerer"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "70db9a0b",
+   "metadata": {},
+   "source": [
+    " This notebook will implement a Gradio UI, streaming, use of the system prompt to add expertise, and the ability to switch between models."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "df46689d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# imports\n",
+    "import os\n",
+    "import json\n",
+    "from dotenv import load_dotenv\n",
+    "from openai import OpenAI\n",
+    "import gradio as gr\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c7416a2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialization\n",
+    "load_dotenv(override=True)\n",
+    "\n",
+    "openai_api_key = os.getenv('OPENAI_API_KEY')\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",
+    "MODEL = \"gpt-4.1-mini\"\n",
+    "openai = OpenAI()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "86966749",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "system_message = \"\"\"\n",
+    "You are an expert technical question answerer specializing in data science, programming, \n",
+    "and software engineering. Your goal is to provide clear, accurate, and practical answers \n",
+    "to technical questions.\n",
+    "\n",
+    "When answering:\n",
+    "- Break down complex concepts into understandable explanations\n",
+    "- Provide code examples when relevant, with comments explaining key parts\n",
+    "- Mention common pitfalls or best practices\n",
+    "- If a question is ambiguous, state your assumptions or ask for clarification\n",
+    "- For debugging questions, explain both the fix and why the error occurred\n",
+    "- Cite specific documentation or resources when helpful\n",
+    "\n",
+    "Always prioritize accuracy and clarity over speed. If you're unsure about something, \n",
+    "acknowledge the uncertainty rather than guessing.\n",
+    "\"\"\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d34e5b81",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Streaming chat funcion\n",
+    "def chat(model, history):\n",
+    "    messages = [{\"role\": \"system\", \"content\": system_message}]\n",
+    "    for h in history:\n",
+    "        messages.append({\"role\": h[\"role\"], \"content\": h[\"content\"]})\n",
+    "\n",
+    "    stream = openai.chat.completions.create(\n",
+    "        model=model, \n",
+    "        messages=messages,\n",
+    "        stream=True\n",
+    "    )\n",
+    "\n",
+    "    response = \"\"\n",
+    "    for chunk in stream:\n",
+    "        if chunk.choices[0].delta.content is not None:\n",
+    "            response += chunk.choices[0].delta.content\n",
+    "            yield history + [{\"role\": \"assistant\", \"content\": response}]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32350869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Gradio Interface\n",
+    "with gr.Blocks() as ui:\n",
+    "    with gr.Row():\n",
+    "        chatbot = gr.Chatbot(height=500, type=\"messages\")\n",
+    "    with gr.Row():\n",
+    "        message = gr.Textbox(label=\"Chat with AI Assistant: \")\n",
+    "        model_dropdown = gr.Dropdown(\n",
+    "            choices=[\"gpt-4.1-mini\",\"gpt-4o-mini\", \"gpt-4o\", \"gpt-4-turbo\"], \n",
+    "            value=\"gpt-4.1-mini\", \n",
+    "            label=\"Select Model\"\n",
+    "        ) \n",
+    "\n",
+    "    def handle_submit(user_message, chat_history):\n",
+    "        # Add user message to history\n",
+    "        chat_history = chat_history + [{\"role\": \"user\", \"content\": user_message}]\n",
+    "        return \"\", chat_history\n",
+    "\n",
+    "    message.submit(\n",
+    "        handle_submit, \n",
+    "        inputs=[message, chatbot], \n",
+    "        outputs=[message, chatbot]\n",
+    "    ).then(\n",
+    "        chat, \n",
+    "        inputs=[model_dropdown, chatbot],\n",
+    "        outputs=[chatbot]\n",
+    "    )\n",
+    "\n",
+    "ui.launch(inbrowser=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cf2b29e1",
+   "metadata": {},
+   "source": [
+    "### Concluding Remarks\n",
+    "In this exercise, we successfully built a working AI chatbot with Gradio that includes streaming responses and the ability to switch between different models. The implementation demonstrates how to create an interactive interface for LLM applications."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
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+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
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+    "version": 3
+   },
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+   "mimetype": "text/x-python",
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+ "nbformat_minor": 5
+}