week2 assignment full checked by hope ogbons

week2/community-contributions/SamuelAdebodun/README.md

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+# Technical Q&A (Week 2)
+
+Web app that streams answers from **OpenAI (gpt-4o-mini)** or **Anthropic (Claude)**. Users pick a model in a dropdown, type a question, and see streamed Markdown in the UI. Built with Gradio; run cells in order and use the share link to open the app.
+
+**Requirements:** `.env` with `OPENAI_API_KEY` and `ANTHROPIC_API_KEY`.
+
+---
+
+## Improvements over Week 1
+
+| Week 1 | Week 2 |
+|--------|--------|
+| Notebook-only: run cells, hardcoded or `input()` question | **Gradio UI**: type any question, choose model, get streamed answer in the browser |
+| Two separate flows (OpenAI cell, Ollama cell) | **Single flow**: one `answer()` + `MODEL_CHOICES`; switch provider via dropdown |
+| OpenAI + Ollama (local) | **OpenAI + Anthropic** (both cloud; Anthropic via OpenAI-compatible API) |
+| `update_display()` in Jupyter | **Streaming to Gradio** `gr.Markdown` with copy button |
+| One question per run | **Reusable app**: submit many questions without re-running cells |

week2/community-contributions/SamuelAdebodun/week2.ipynb

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+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Dependencies: env, OpenAI client, and Gradio for the web UI\n",
+        "import os\n",
+        "import json\n",
+        "import sqlite3\n",
+        "from dotenv import load_dotenv\n",
+        "from openai import OpenAI\n",
+        "import gradio as gr"
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "74dc0ebc"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Model IDs used for OpenAI and Anthropic API calls\n",
+        "MODEL_GPT = \"gpt-4o-mini\"\n",
+        "MODEL_CLAUDE = \"claude-sonnet-4-5-20250929\""
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "8bd6d9c4"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "load_dotenv(override=True)\n",
+        "openai_key = os.getenv('OPENAI_API_KEY')\n",
+        "anthropic_key = os.getenv('ANTHROPIC_API_KEY')\n",
+        "\n",
+        "if openai_key:\n",
+        "    print(f\"OpenAI API Key exists and begins {openai_key[:8]}\")\n",
+        "else:\n",
+        "    print(\"OpenAI API Key not set\")\n",
+        "\n",
+        "if anthropic_key:\n",
+        "    print(f\"Anthropic API Key exists and begins {anthropic_key[:7]}\")\n",
+        "else:\n",
+        "    print(\"Anthropic API Key not set\")\n"
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "c72d0de1"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Create OpenAI and Anthropic clients (Anthropic via OpenAI-compatible base URL)\n",
+        "openai_client = OpenAI()\n",
+        "anthropic_client = OpenAI(api_key=anthropic_key, base_url=\"https://api.anthropic.com/v1\")\n",
+        "print ( \"clients are ready!!\")"
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "6ad8c068"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# System prompt that defines the assistant's role for all model calls\n",
+        "system_prompt = \"\"\"You are a helpful technical tutor who answers questions about \n",
+        "Python code, software engineering, devops, LLMs and Cloud engineering .\"\"\""
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "e6ceaba8"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Generator functions: stream chat completion chunks from OpenAI and Anthropic\n",
+        "def stream_openai(messages):\n",
+        "    \"\"\"Stream completion chunks from OpenAI.\"\"\"\n",
+        "    stream = openai_client.chat.completions.create(\n",
+        "        model=MODEL_GPT,\n",
+        "        messages=messages,\n",
+        "        stream=True,\n",
+        "    )\n",
+        "    for chunk in stream:\n",
+        "        if chunk.choices and chunk.choices[0].delta.content:\n",
+        "            yield chunk.choices[0].delta.content\n",
+        "\n",
+        "\n",
+        "def stream_anthropic(messages):\n",
+        "    \"\"\"Stream completion chunks from Anthropic (via OpenAI-compatible API).\"\"\"\n",
+        "    stream = anthropic_client.chat.completions.create(\n",
+        "        model=MODEL_CLAUDE,\n",
+        "        messages=messages,\n",
+        "        stream=True,\n",
+        "    )\n",
+        "    for chunk in stream:\n",
+        "        if chunk.choices and chunk.choices[0].delta.content:\n",
+        "            yield chunk.choices[0].delta.content"
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "37a7313d"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "\n",
+        "# Single source of truth: (display label, stream_fn)\n",
+        "MODEL_CHOICES = [\n",
+        "    (\"OpenAI (gpt-4o-mini)\", stream_openai),\n",
+        "    (\"Anthropic (claude-sonnet-4-5-20250929)\", stream_anthropic),\n",
+        "]\n",
+        "\n",
+        "\n",
+        "def answer(question, model_choice):\n",
+        "    \"\"\"Stream an answer for the chosen model.\"\"\"\n",
+        "    if not (question and question.strip()):\n",
+        "        yield \"Please enter a question.\"\n",
+        "        return\n",
+        "    messages = [\n",
+        "        {\"role\": \"system\", \"content\": system_prompt},\n",
+        "        {\"role\": \"user\", \"content\": question.strip()},\n",
+        "    ]\n",
+        "    choice_map = dict(MODEL_CHOICES)\n",
+        "    stream_fn = choice_map.get(model_choice, stream_openai)\n",
+        "    acc = \"\"\n",
+        "    for chunk in stream_fn(messages):\n",
+        "        acc += chunk\n",
+        "        yield acc"
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "080503f0"
+    },
+    {
+      "cell_type": "code",
+      "metadata": {},
+      "source": [
+        "# Gradio UI: model dropdown, question textbox, streamed Markdown output; Submit + Enter trigger answer()\n",
+        "with gr.Blocks(title=\"Technical Q&A\") as demo:\n",
+        "    gr.Markdown(\"## Technical Q&A — Ask a question, pick a model, get a streamed answer\")\n",
+        "    model_dropdown = gr.Dropdown(\n",
+        "        choices=[\"OpenAI (gpt-4o-mini)\", \"Anthropic (claude-sonnet-4-5-20250929)\"],\n",
+        "        value=\"OpenAI (gpt-4o-mini)\",\n",
+        "        label=\"Model\",\n",
+        "    )\n",
+        "    question_in = gr.Textbox(\n",
+        "        placeholder=\"e.g. Explain: yield from {book.get('author') for book in books if book.get('author')}\",\n",
+        "        label=\"Question\",\n",
+        "        lines=3,\n",
+        "    )\n",
+        "\n",
+        "    gr.Markdown(\"### Answer\")\n",
+        "    answer_out = gr.Markdown(value=\"*Answer will appear here.*\", show_copy_button=True)\n",
+        "\n",
+        "    question_in.submit(\n",
+        "        answer,\n",
+        "        inputs=[question_in, model_dropdown],\n",
+        "        outputs=answer_out,\n",
+        "    )\n",
+        "    gr.Button(\"Submit\").click(\n",
+        "        answer,\n",
+        "        inputs=[question_in, model_dropdown],\n",
+        "        outputs=answer_out,\n",
+        "    )\n",
+        "\n",
+        "demo.launch(share=True)"
+      ],
+      "execution_count": null,
+      "outputs": [],
+      "id": "839d56a5"
+    }
+  ],
+  "metadata": {
+    "kernelspec": {
+      "display_name": ".venv",
+      "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": "3.12.12"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 5
+}