Update notebooks/demo_notebook.ipynb

notebooks/demo_notebook.ipynb

@@ -1,67 +1,103 @@
 {
-"cells": [
-{
-"cell_type": "markdown",
-"metadata": {},
-"source": [
-"# Shasha Demo Notebook\n",
-"This notebook shows how to programmatically invoke our AI inference pipeline, run sentiment analysis, and generate code examples."
-]
-},
-{
-"cell_type": "code",
-"execution_count": null,
-"metadata": {},
-"outputs": [],
-"source": [
-"# 1. Setup inference client\n",
-"from hf_client import get_inference_client\n",
-"# Initialize client for Qwen3-32B (fallback on Groq if unavailable)\n",
-"client = get_inference_client('Qwen/Qwen3-32B', provider='auto')\n",
-"# Example chat completion request\n",
-"resp = client.chat.completions.create(\n",
-"    model='Qwen/Qwen3-32B',\n",
-"    messages=[{'role':'user','content':'Write a Python function to reverse a string.'}]\n",
-")\n",
-"print(resp.choices[0].message.content)"
-]
-},
-{
-"cell_type": "code",
-"execution_count": null,
-"metadata": {},
-"outputs": [],
-"source": [
-"# 2. Sentiment Analysis with Transformers.js (Python demo)\n",
-"from transformers import pipeline\n",
-"# Using OpenAI provider for sentiment\n",
-"sentiment = pipeline('sentiment-analysis', model='openai/gpt-4', trust_remote_code=True)\n",
-"print(sentiment('I love building AI-powered tools!'))"
-]
-},
-{
-"cell_type": "markdown",
-"metadata": {},
-"source": [
-"---\n",
-"## Next steps:\n",
-"- Try different models (Gemini Pro, Fireworks AI) by changing the model= parameter.\n",
-"- Explore custom plugins via plugins.py to integrate with Slack or GitHub.\n",
-"- Use auth.py to load private files from Google Drive."
-]
-}
-],
-"metadata": {
-"kernelspec": {
-"display_name": "Python 3",
-"language": "python",
-"name": "python3"
-},
-"language_info": {
-"name": "python",
-"version": "3.x"
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# AnyCoder Demo Notebook\n",
+    "\n",
+    "This notebook illustrates how to:\n",
+    "\n",
+    "1. **Instantiate** the unified `hf_client` with automatic provider routing.\n",
+    "2. **Call** a chat completion (Groq → OpenAI → Gemini fall‑back).\n",
+    "3. **Trigger** the FastAPI `/predict` endpoint served by *app.py*.\n",
+    "4. **Run** a quick sentiment‑analysis pipeline using your preferred provider."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 1. Setup inference client\n",
+    "from hf_client import get_inference_client\n",
+    "\n",
+    "# Choose a model (will route to best provider according to prefix rules)\n",
+    "model_id = 'openai/gpt-4'   # try 'gemini/pro' or any HF model path\n",
+    "client = get_inference_client(model_id, provider='auto')\n",
+    "\n",
+    "# Simple chat completion\n",
+    "resp = client.chat.completions.create(\n",
+    "    model=model_id,\n",
+    "    messages=[{'role': 'user', 'content': 'Write a Python function to reverse a string.'}]\n",
+    ")\n",
+    "print(resp.choices[0].message.content)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 2. Sentiment analysis via HF Inference Providers (OpenAI GPT‑4)\n",
+    "from transformers import pipeline\n",
+    "\n",
+    "sentiment = pipeline(\n",
+    "    'sentiment-analysis', \n",
+    "    model='openai/gpt-4',      # could be 'gemini/pro' etc.\n",
+    "    trust_remote_code=True\n",
+    ")\n",
+    "sentiment('I love building AI‑powered tools!')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# 3. Call the FastAPI /predict endpoint exposed by app.py\n",
+    "import json, requests\n",
+    "\n",
+    "payload = {\n",
+    "    'prompt': 'Generate a minimal HTML page.',\n",
+    "    'model_id': 'gemini/pro',\n",
+    "    'language': 'html',\n",
+    "    'web_search': False\n",
+    "}\n",
+    "\n",
+    "r = requests.post('http://localhost:7860/predict', json=payload)\n",
+    "print('Status:', r.status_code)\n",
+    "print(json.loads(r.text)['code'][:400])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "## Next steps\n",
+    "\n",
+    "* Switch `model_id` to **`'gemini/pro'`**, **`'fireworks-ai/fireworks-v1'`**, or any HF model (e.g. `Qwen/Qwen3-32B`)—routing will adjust automatically.\n",
+    "* Explore **`plugins.py`** for Slack / GitHub integrations.\n",
+    "* Use **`auth.py`** helpers to pull private Google Drive docs into the pipeline.\n",
+    "* Extend `/predict` with temperature, max‑tokens, or stream support."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.x"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
 }
-},
-"nbformat": 4,
-"nbformat_minor": 5
-}