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{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import anthropic\n",
"import openai\n",
"import ipywidgets as widgets\n",
"from IPython.display import display, Markdown, update_display\n",
"from dotenv import load_dotenv\n",
"import requests\n",
"import json\n",
"\n",
"MODEL_CLAUDE = 'claude-3-5-sonnet-20241022'\n",
"MODEL_LLAMA = 'llama3.2'\n",
"MODEL_GPT = 'gpt-4o-mini'\n",
"\n",
"load_dotenv()\n",
"\n",
"# Define models\n",
"models = [\n",
" ('Claude (Anthropic)', MODEL_CLAUDE),\n",
" ('LLaMA (Meta)', MODEL_LLAMA),\n",
" ('GPT (OpenAI)', MODEL_GPT)\n",
"]\n",
"\n",
"model_dropdown = widgets.Dropdown(\n",
" options=[('', None)] + [(model[0], model[0]) for model in models],\n",
" value=None,\n",
" placeholder='Choose a model',\n",
" description='Model:',\n",
" style={'description_width': 'initial'}\n",
")\n",
"\n",
"selected_model = \"\"\n",
"\n",
"text = input(f\"Hello, I am your personal tutor. Please ask me a question regarding your code:\")\n",
"\n",
"system_prompt = \"You are a helpful technical tutor who answers questions about programming, software engineering, data science and LLMs\"\n",
"user_prompt = \"Please give a detailed explanation to the following question: \" + text\n",
"\n",
"messages = [\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": user_prompt}\n",
"]\n",
"\n",
"# Get gpt-4o-mini to answer, with streaming\n",
"def get_gpt_response():\n",
" stream = openai.chat.completions.create(model=MODEL_GPT, messages=messages,stream=True)\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {response}\"), display_id=display_handle.display_id)\n",
" return response\n",
"\n",
"# Get Llama 3.2 to answer, with streaming\n",
"def get_llama_response():\n",
" api_url = \"http://localhost:11434/api/chat\"\n",
" payload = {\n",
" \"model\": MODEL_LLAMA,\n",
" \"messages\": messages,\n",
" \"stream\": True\n",
" }\n",
" response = requests.post(api_url, json=payload, stream=True)\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" result = \"\"\n",
" \n",
" for line in response.iter_lines():\n",
" if line:\n",
" json_response = json.loads(line)\n",
" if \"message\" in json_response:\n",
" content = json_response[\"message\"].get(\"content\", \"\")\n",
" result += content\n",
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n",
" if json_response.get(\"done\", False):\n",
" break\n",
" \n",
" return result\n",
"\n",
"# Get Claude 3.5 to answer, with streaming\n",
"def get_claude_response():\n",
" client = anthropic.Anthropic()\n",
"\n",
" response = client.messages.create(\n",
" model=MODEL_CLAUDE,\n",
" system=system_prompt,\n",
" messages=[\n",
" {\n",
" \"role\": \"user\",\n",
" \"content\": user_prompt\n",
" }\n",
" ],\n",
" stream=True,\n",
" max_tokens=8192,\n",
" temperature=1,\n",
" )\n",
" result = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
"\n",
" for chunk in response:\n",
" # Check if the chunk is a ContentBlockDeltaEvent\n",
" if hasattr(chunk, 'delta') and hasattr(chunk.delta, 'text'):\n",
" result += chunk.delta.text\n",
" update_display(Markdown(f\"**Question:** {text}\\n\\n**Answer:** {result}\"), display_id=display_handle.display_id)\n",
" return result\n",
"\n",
"def on_text_submit():\n",
" try:\n",
" if 'Claude' in selected_model:\n",
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n",
" get_claude_response()\n",
" elif 'LLaMA' in selected_model:\n",
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n",
" get_llama_response()\n",
" elif 'GPT' in selected_model:\n",
" display(Markdown(f\"# **Selected model: {selected_model}**\"))\n",
" get_gpt_response()\n",
" except Exception as e:\n",
" display(Markdown(f\"**Error:** {str(e)}\"))\n",
"\n",
"def on_model_select(change):\n",
" global selected_model\n",
"\n",
" selected_model = change['new'].split(' ')[0]\n",
" if selected_model is not None:\n",
" on_text_submit()\n",
" return change['new'].split(' ')[0]\n",
"\n",
"# Register callbacks\n",
"model_dropdown.observe(on_model_select, names='value')\n",
"\n",
"display(model_dropdown)"
]
}
],
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"file_extension": ".py",
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"nbconvert_exporter": "python",
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}