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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🧪 Relational Ai for Nursing Multi-Judge Evaluation\n",
"\n",
"**Judges:**\n",
"- 🔵 **GPT-5.2** (Azure OpenAI)\n",
"- 🟢 **Gemini** (Google AI - will try 3 Pro, fallback to 2.5 Pro)\n",
"\n",
"**Model:** `NurseCitizenDeveloper/nursing-llama-3-8b-fons`"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 1. Install Dependencies\n",
"!pip install -U bitsandbytes transformers accelerate openai langchain-google-genai google-generativeai -q\n",
"print(\"✅ Installed! Restart runtime if needed, then run Cell 2\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 2. Load Model from Hugging Face\n",
"import torch\n",
"from transformers import AutoModelForCausalLM, AutoTokenizer\n",
"\n",
"HF_MODEL = \"NurseCitizenDeveloper/nursing-llama-3-8b-fons\"\n",
"print(f\"🔄 Loading model: {HF_MODEL}\")\n",
"\n",
"tokenizer = AutoTokenizer.from_pretrained(HF_MODEL)\n",
"model = AutoModelForCausalLM.from_pretrained(HF_MODEL, device_map=\"auto\", torch_dtype=torch.float16)\n",
"print(\"✅ Model loaded!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 3. Setup GPT-5.2 Judge (Azure)\n",
"from openai import AzureOpenAI\n",
"\n",
"gpt5_client = AzureOpenAI(\n",
" api_version=\"2024-12-01-preview\",\n",
" azure_endpoint=\"https://ai-lincoln0303ai530606275924.cognitiveservices.azure.com/\",\n",
" api_key=\"YOUR_AZURE_OPENAI_API_KEY\" # Secret removed for security\n",
")\n",
"print(\"✅ GPT-5.2 Judge ready!\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 4. Setup Gemini Judge (Google) - Auto-detects best available model\n",
"import os\n",
"import google.generativeai as genai\n",
"\n",
"os.environ[\"GOOGLE_API_KEY\"] = \"YOUR_GEMINI_API_KEY\" # Secret removed for security\n",
"genai.configure(api_key=os.environ[\"GOOGLE_API_KEY\"])\n",
"\n",
"# Try Gemini 3 Pro first, fallback to 2.5 Pro\n",
"gemini_model_name = None\n",
"for model_name in [\"gemini-3-pro\", \"gemini-2.5-pro\", \"gemini-2.0-flash\", \"gemini-1.5-pro\"]:\n",
" try:\n",
" test_model = genai.GenerativeModel(model_name)\n",
" test_model.generate_content(\"test\")\n",
" gemini_model_name = model_name\n",
" print(f\"✅ Gemini Judge ready: {model_name}\")\n",
" break\n",
" except Exception as e:\n",
" print(f\"⚠️ {model_name} not available: {str(e)[:50]}\")\n",
"\n",
"if gemini_model_name:\n",
" gemini_judge = genai.GenerativeModel(gemini_model_name)\n",
"else:\n",
" print(\"❌ No Gemini model available\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 5. Define Test Cases\n",
"alpaca_prompt = \"\"\"Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n",
"\n",
"### Instruction:\n",
"{}\n",
"\n",
"### Input:\n",
"{}\n",
"\n",
"### Response:\n",
"{}\"\"\"\n",
"\n",
"test_cases = [\n",
" {\"instruction\": \"Summarize key nursing interventions for a patient with delirium.\",\n",
" \"input\": \"Patient is an 85-year-old male with acute confusion and visual hallucinations.\"},\n",
" {\"instruction\": \"What are the FONS principles for person-centred care?\",\n",
" \"input\": \"A nurse is documenting care for a patient with dementia.\"},\n",
" {\"instruction\": \"Explain why skin tone documentation is important in pressure ulcer risk assessment.\",\n",
" \"input\": \"Using the Braden Scale for a patient with darker skin.\"},\n",
" {\"instruction\": \"Describe the ADPIE nursing process.\",\n",
" \"input\": \"Training a new nursing student on documentation.\"},\n",
"]\n",
"\n",
"eval_prompt_template = \"\"\"You are an expert nursing educator. Evaluate this AI response on a scale of 1-10:\n",
"\n",
"1. **Clinical Accuracy** (1-10): Is the information clinically correct?\n",
"2. **Person-Centred Language** (1-10): Does it use respectful, dignified language?\n",
"3. **FONS Alignment** (1-10): Does it reflect FONS principles (relational care, practice development)?\n",
"\n",
"**Instruction:** {instruction}\n",
"**Context:** {context}\n",
"**Model Response:** {response}\n",
"\n",
"Provide ONLY the three scores in this exact format:\n",
"Accuracy: X/10\n",
"Person-Centred: X/10\n",
"FONS: X/10\n",
"Brief rationale:\"\"\"\n",
"\n",
"print(f\"📋 {len(test_cases)} test cases loaded\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 6. Run Multi-Judge Evaluation\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(f\"🏁 Relational Ai for Nursing MULTI-JUDGE EVALUATION (GPT-5.2 vs {gemini_model_name})\")\n",
"print(\"=\"*70)\n",
"\n",
"results = []\n",
"\n",
"for i, case in enumerate(test_cases, 1):\n",
" print(f\"\\n{'='*70}\")\n",
" print(f\"Test {i}/{len(test_cases)}: {case['instruction']}\")\n",
" print(\"=\"*70)\n",
" \n",
" # Generate response from our model\n",
" prompt = alpaca_prompt.format(case[\"instruction\"], case[\"input\"], \"\")\n",
" inputs = tokenizer(prompt, return_tensors=\"pt\").to(model.device)\n",
" with torch.no_grad():\n",
" outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, temperature=0.7)\n",
" response = tokenizer.decode(outputs[0], skip_special_tokens=True).split(\"### Response:\")[-1].strip()\n",
" \n",
" print(f\"\\n🤖 Model Response: {response[:250]}...\")\n",
" \n",
" eval_prompt = eval_prompt_template.format(\n",
" instruction=case[\"instruction\"],\n",
" context=case[\"input\"],\n",
" response=response\n",
" )\n",
" \n",
" # GPT-5.2 Evaluation\n",
" print(\"\\n🔵 GPT-5.2 Judge:\")\n",
" try:\n",
" gpt5_response = gpt5_client.chat.completions.create(\n",
" model=\"gpt-5.2-chat\",\n",
" messages=[{\"role\": \"user\", \"content\": eval_prompt}],\n",
" max_tokens=500\n",
" )\n",
" gpt5_eval = gpt5_response.choices[0].message.content\n",
" print(gpt5_eval)\n",
" except Exception as e:\n",
" print(f\"Error: {e}\")\n",
" gpt5_eval = \"N/A\"\n",
" \n",
" # Gemini Evaluation\n",
" print(f\"\\n🟢 {gemini_model_name} Judge:\")\n",
" try:\n",
" gemini_response = gemini_judge.generate_content(eval_prompt)\n",
" gemini_eval = gemini_response.text\n",
" print(gemini_eval)\n",
" except Exception as e:\n",
" print(f\"Error: {e}\")\n",
" gemini_eval = \"N/A\"\n",
" \n",
" results.append({\n",
" \"test\": case[\"instruction\"],\n",
" \"response\": response,\n",
" \"gpt5\": gpt5_eval,\n",
" \"gemini\": gemini_eval\n",
" })\n",
"\n",
"print(\"\\n\" + \"=\"*70)\n",
"print(\"✅ MULTI-JUDGE EVALUATION COMPLETE\")\n",
"print(\"=\"*70)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# 7. Summary Comparison\n",
"print(f\"\\n📊 SUMMARY: GPT-5.2 vs {gemini_model_name}\")\n",
"print(\"=\"*60)\n",
"for i, r in enumerate(results, 1):\n",
" print(f\"\\n--- Test {i}: {r['test'][:40]}... ---\")\n",
" print(f\"\\n🔵 GPT-5.2:\\n{r['gpt5'][:300]}\")\n",
" print(f\"\\n🟢 Gemini:\\n{r['gemini'][:300]}\")\n",
" print(\"=\"*60)"
]
}
],
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