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week4/community-contributions/w4d3_add_models.ipynb Normal file
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{
"cells": [
{
"cell_type": "markdown",
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
"metadata": {},
"source": [
"# Code Generator\n",
"\n",
"The requirement: use a Frontier model to generate high performance C++ code from Python code\n",
"\n",
"- Try adding Gemini to the Closed Source mix\n",
"- Try more open-source models such as CodeLlama and StarCoder, and see if you can get CodeGemma to work\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import io\n",
"import sys\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"# import google.generativeai\n",
"from google import genai\n",
"from google.genai import types\n",
"import anthropic\n",
"import ollama\n",
"from IPython.display import Markdown, display, update_display\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f672e1c-87e9-4865-b760-370fa605e614",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
"metadata": {},
"outputs": [],
"source": [
"# initialize\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"# google.generativeai.configure()\n",
"client = genai.Client()\n",
"\n",
"\n",
"OPENAI_MODEL = \"gpt-4o\"\n",
"CLAUDE_MODEL = \"claude-sonnet-4-20250514\"\n",
"GEMINI_MODEL = 'gemini-2.5-flash'\n",
"LLAMA_MODEL = \"llama3.2\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6896636f-923e-4a2c-9d6c-fac07828a201",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"You are an assistant that reimplements Python code in high performance C++ for an M1 Mac. \"\n",
"system_message += \"Respond only with C++ code; use comments sparingly and do not provide any explanation other than occasional comments. \"\n",
"system_message += \"The C++ response needs to produce an identical output in the fastest possible time. \"\n",
"system_message += \"Do not include Markdown formatting (```), language tags (cpp), or extra text. \""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for(python):\n",
" user_prompt = \"Rewrite this Python code in C++ with the fastest possible implementation that produces identical output in the least time. \"\n",
" user_prompt += \"Respond only with C++ code; do not explain your work other than a few comments. \"\n",
" user_prompt += \"\"\"Your response must not contain neither the leading \"```cpp\" nor the trailer: \"```\" strings! \"\"\"\n",
" user_prompt += \"Pay attention to number types to ensure no int overflows. Remember to #include all necessary C++ packages such as iomanip.\\n\\n\"\n",
" user_prompt += python\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6190659-f54c-4951-bef4-4960f8e51cc4",
"metadata": {},
"outputs": [],
"source": [
"def messages_for(python):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt_for(python)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "71e1ba8c-5b05-4726-a9f3-8d8c6257350b",
"metadata": {},
"outputs": [],
"source": [
"# write to a file called optimized.cpp\n",
"\n",
"def write_output(cpp):\n",
" with open(\"optimized.cpp\", \"w\") as f:\n",
" f.write(cpp)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "a1cbb778-fa57-43de-b04b-ed523f396c38",
"metadata": {},
"outputs": [],
"source": [
"pi = \"\"\"\n",
"import time\n",
"\n",
"def calculate(iterations, param1, param2):\n",
" result = 1.0\n",
" for i in range(1, iterations+1):\n",
" j = i * param1 - param2\n",
" result -= (1/j)\n",
" j = i * param1 + param2\n",
" result += (1/j)\n",
" return result\n",
"\n",
"start_time = time.time()\n",
"result = calculate(100_000_000, 4, 1) * 4\n",
"end_time = time.time()\n",
"\n",
"print(f\"Result: {result:.12f}\")\n",
"print(f\"Execution Time: {(end_time - start_time):.6f} seconds\")\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
"metadata": {},
"outputs": [],
"source": [
"python_hard = \"\"\"# Be careful to support large number sizes\n",
"\n",
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
" value = seed\n",
" while True:\n",
" value = (a * value + c) % m\n",
" yield value\n",
"\n",
"def max_subarray_sum(n, seed, min_val, max_val):\n",
" lcg_gen = lcg(seed)\n",
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
" max_sum = float('-inf')\n",
" for i in range(n):\n",
" current_sum = 0\n",
" for j in range(i, n):\n",
" current_sum += random_numbers[j]\n",
" if current_sum > max_sum:\n",
" max_sum = current_sum\n",
" return max_sum\n",
"\n",
"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
" total_sum = 0\n",
" lcg_gen = lcg(initial_seed)\n",
" for _ in range(20):\n",
" seed = next(lcg_gen)\n",
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
" return total_sum\n",
"\n",
"# Parameters\n",
"n = 10000 # Number of random numbers\n",
"initial_seed = 42 # Initial seed for the LCG\n",
"min_val = -10 # Minimum value of random numbers\n",
"max_val = 10 # Maximum value of random numbers\n",
"\n",
"# Timing the function\n",
"import time\n",
"start_time = time.time()\n",
"result = total_max_subarray_sum(n, initial_seed, min_val, max_val)\n",
"end_time = time.time()\n",
"\n",
"print(\"Total Maximum Subarray Sum (20 runs):\", result)\n",
"print(\"Execution Time: {:.6f} seconds\".format(end_time - start_time))\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0be9f47d-5213-4700-b0e2-d444c7c738c0",
"metadata": {},
"outputs": [],
"source": [
"def stream_gpt(python):\n",
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for(python), stream=True)\n",
" reply = \"\"\n",
" for chunk in stream:\n",
" fragment = chunk.choices[0].delta.content or \"\"\n",
" reply += fragment\n",
" yield reply"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8669f56b-8314-4582-a167-78842caea131",
"metadata": {},
"outputs": [],
"source": [
"def stream_claude(python):\n",
" result = claude.messages.stream(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" system=system_message,\n",
" messages=[{\"role\": \"user\", \"content\": user_prompt_for(python)}],\n",
" )\n",
" reply = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" reply += text\n",
" yield reply"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97205162",
"metadata": {},
"outputs": [],
"source": [
"def stream_gemini(python):\n",
" response = client.models.generate_content_stream(\n",
" model=GEMINI_MODEL,\n",
" config=types.GenerateContentConfig(\n",
" system_instruction=system_message),\n",
" contents=user_prompt_for(python)\n",
" )\n",
"\n",
" reply = \"\"\n",
" for chunk in response:\n",
" fragment = chunk.text or \"\"\n",
" reply += fragment\n",
" yield reply\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f94b13e",
"metadata": {},
"outputs": [],
"source": [
"def stream_llama_local(python):\n",
" stream = ollama.chat(\n",
" model='llama3.2',\n",
" messages=messages_for(python),\n",
" stream=True,\n",
" )\n",
"\n",
" reply = \"\"\n",
" # Iterate through the streamed chunks and print the content\n",
" for chunk in stream:\n",
" #print(chunk['message']['content'], end='', flush=True)\n",
" if 'content' in chunk['message']:\n",
" fragment = chunk['message']['content']\n",
" reply += fragment\n",
" yield reply\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d",
"metadata": {},
"outputs": [],
"source": [
"def optimize(python, model):\n",
" if model==\"GPT\":\n",
" result = stream_gpt(python)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(python)\n",
" elif model==\"Gemini\":\n",
" result = stream_gemini(python)\n",
" elif model==\"Llama\":\n",
" result = stream_llama_local(python)\n",
"\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" for stream_so_far in result:\n",
" yield stream_so_far"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a",
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n",
" cpp = gr.Textbox(label=\"C++ code, copy it here: https://www.programiz.com/cpp-programming/online-compiler/\", lines=10)\n",
" with gr.Row():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\", \"Llama\"], label=\"Select model\", value=\"GPT\")\n",
" convert = gr.Button(\"Convert code\")\n",
"\n",
" convert.click(optimize, inputs=[python, model], outputs=[cpp])\n",
"\n",
"ui.launch(inbrowser=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.13"
}
},
"nbformat": 4,
"nbformat_minor": 5
}

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{
"cells": [
{
"cell_type": "markdown",
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
"metadata": {},
"source": [
"# Trading Code Generator\n",
"\n",
"A code generator that writes trading code to buy and sell equities in a simulated environment, based on a given API\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import sys\n",
"import io\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from google import genai\n",
"from google.genai import types\n",
"import anthropic\n",
"import ollama\n",
"import gradio as gr\n",
"import requests\n",
"from typing import Any"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f672e1c-87e9-4865-b760-370fa605e614",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"# initialize\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"client = genai.Client()\n",
"\n",
"\n",
"OPENAI_MODEL = \"gpt-4o\"\n",
"CLAUDE_MODEL = \"claude-sonnet-4-20250514\"\n",
"GEMINI_MODEL = 'gemini-2.5-flash'\n",
"LLAMA_MODEL = \"llama3.2\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "36b0a6f6",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"system_message = \"\"\"\n",
"You are an effective programming assistant specialized to generate Python code based on the inputs.\n",
"Respond only with Python code; use comments sparingly and do not provide any explanation other than occasional comments.\n",
"Do not include Markdown formatting (```), language tags (python), or extra text.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def get_user_prompt_for_trade_code_generation(currency, wallet_balance):\n",
" wallet_balance = str(wallet_balance)\n",
"\n",
" user_prompt = \"\"\"\n",
" Create a simple Crypto trading engine Python code.\n",
" The engine will sell or buy the given crypto currency against USDT (Tether) based on the available wallet balance\n",
" This should be a simple Python code, not a function\n",
" The currency is: {}\n",
" The wallet balance is: {}\"\"\".format(currency, wallet_balance)\n",
" user_prompt += \"\"\"\n",
" Output will be a text containing the followings:\n",
" - advice to sell or buy\n",
" - amount in USDT\n",
" Rules you have to apply in the code:\n",
" - compose symbol: convert the input `crypto_currency` argument to upper case and concatenate it to string \"USDT\"\n",
" - compose url passing the previously composed symbol: `url = f\"https://data-api.binance.vision/api/v3/ticker/24hr?symbol={symbol}`\n",
" - call the api from with this url, expect to get the following json response, for example:\n",
" {'symbol': 'BTCUSDT',\n",
" 'priceChange': '1119.99000000',\n",
" 'priceChangePercent': '0.969',\n",
" 'weightedAvgPrice': '116314.23644195',\n",
" 'prevClosePrice': '115600.00000000',\n",
" 'lastPrice': '116719.99000000',\n",
" 'lastQty': '0.05368000',\n",
" 'bidPrice': '116719.99000000',\n",
" 'bidQty': '2.81169000',\n",
" 'askPrice': '116720.00000000',\n",
" 'askQty': '3.46980000',\n",
" 'openPrice': '115600.00000000',\n",
" 'highPrice': '117286.73000000',\n",
" 'lowPrice': '114737.11000000',\n",
" 'volume': '12500.51369000',\n",
" 'quoteVolume': '1453987704.98443060',\n",
" 'openTime': 1758015394001,\n",
" 'closeTime': 1758101794001,\n",
" 'firstId': 5236464586,\n",
" 'lastId': 5238628513,\n",
" 'count': 2163928}\n",
" - build a logic based on the retrieving values which can decide whether the engine should sell or buy he given crypto currency\n",
" - in the logic the code should also decide what is the confident level of selling or buying.\n",
" - if the confident level is high the `amount` should be higher (closer to the `current_wallet_balance`)\n",
" - if the confident level is lower then the amount should be lower as well\n",
" - error handling:\n",
" - if the api call returns with a json hving `code`, `msg` keys in it (eg. 'code': -1121, 'msg': 'Invalid symbol.') then handle this error message\n",
" Response rule: in your response do not include Markdown formatting (```), language tags (python), or extra text.\n",
" \"\"\"\n",
" return user_prompt\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5030fdf5",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def get_user_prompt_for_docstring_generation(python_code):\n",
" return f\"\"\"\n",
" Consider the following Python code: \\n\\n\n",
" {python_code} \\n\\n\n",
"\n",
" Generate a docstring comment around this code and it alongside with the Python code. \\n\n",
" Response rule: in your response do not include Markdown formatting (```), language tags (python), or extra text.\n",
"\n",
" \"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8dc065c2",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def trade_gpt(currency, wallet_balance):\n",
" completion = openai.chat.completions.create(\n",
" model=OPENAI_MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": get_user_prompt_for_trade_code_generation(\n",
" currency,\n",
" wallet_balance\n",
" )\n",
" }\n",
" ]\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b402c67",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def code_comment_gpt(python_code):\n",
" completion = openai.chat.completions.create(\n",
" model=OPENAI_MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": get_user_prompt_for_docstring_generation(python_code)}\n",
" ]\n",
" )\n",
" return completion.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0dc80287",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def trade_cloude(currency, wallet_balance):\n",
" message = claude.messages.create(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": get_user_prompt_for_trade_code_generation(\n",
" currency,\n",
" wallet_balance\n",
" )\n",
" },\n",
" ],\n",
" )\n",
"\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "90eb9547",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def code_comment_cloude(python_code):\n",
" message = claude.messages.create(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" temperature=0.7,\n",
" system=system_message,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": get_user_prompt_for_docstring_generation(python_code)\n",
" },\n",
" ],\n",
" )\n",
"\n",
" return message.content[0].text"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b94fbd55",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"\n",
"def trade_gemini(currency, wallet_balance):\n",
" response = client.models.generate_content(\n",
" model=GEMINI_MODEL,\n",
" config=types.GenerateContentConfig(\n",
" system_instruction=system_message),\n",
" contents=get_user_prompt_for_trade_code_generation(\n",
" currency,\n",
" wallet_balance\n",
" )\n",
" )\n",
"\n",
" return response.text\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f83ef7b8",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"\n",
"def code_comment_gemini(python_code):\n",
" response = client.models.generate_content(\n",
" model=GEMINI_MODEL,\n",
" config=types.GenerateContentConfig(\n",
" system_instruction=system_message),\n",
" contents=get_user_prompt_for_docstring_generation(python_code)\n",
" )\n",
"\n",
" return response.text\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6737962d",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def trade_llama(currency, wallet_balance):\n",
" completion = ollama.chat(\n",
" model=LLAMA_MODEL,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": get_user_prompt_for_trade_code_generation(\n",
" currency,\n",
" wallet_balance\n",
" )\n",
" },\n",
" ],\n",
" )\n",
"\n",
" return completion['message']['content']\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b815aa07",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def code_comment_llama(python_code):\n",
" completion = ollama.chat(\n",
" model=LLAMA_MODEL,\n",
" messages=[\n",
" {\"role\": \"user\", \"content\": get_user_prompt_for_docstring_generation(python_code)},\n",
" ],\n",
" )\n",
"\n",
" return completion['message']['content']"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b9e07437",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def generate_python_code(input_model, currency, wallet_balance):\n",
" model_mapping = {\"GPT\": trade_gpt(currency, wallet_balance),\n",
" \"Claude\": trade_cloude(currency, wallet_balance),\n",
" \"Gemini\": trade_gemini(currency, wallet_balance),\n",
" \"Llama\": trade_llama(currency, wallet_balance)}\n",
" try:\n",
" return model_mapping[input_model]\n",
" except KeyError as e:\n",
" print(f\"{e}: {input_model} is not valid\")\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "016fed0e",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def comment_python_code(input_model, python_code):\n",
" model_mapping = {\"GPT\": code_comment_gpt(python_code),\n",
" \"Claude\": code_comment_cloude(python_code),\n",
" \"Gemini\": code_comment_gemini(python_code),\n",
" \"Llama\": code_comment_llama(python_code)\n",
" }\n",
" try:\n",
" return model_mapping[input_model]\n",
" except KeyError as e:\n",
" print(f\"{e}: {input_model} is not valid\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e224a715",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"def execute_python(code):\n",
" code = code.replace('```python\\n','').replace('```','')\n",
" try:\n",
" output = io.StringIO()\n",
" sys.stdout = output\n",
" exec(code)\n",
" finally:\n",
" sys.stdout = sys.__stdout__\n",
" return output.getvalue()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ea96a88d",
"metadata": {
"vscode": {
"languageId": "python"
}
},
"outputs": [],
"source": [
"with gr.Blocks() as ui:\n",
" gr.Markdown(\"\"\"It generate Trading Python code, which will recommend you whether sell or buy a given crypto currency at its current price.\n",
" Based on the confindency level of the prediction it will recommend what amount should be placed from your available wallet balance\"\"\")\n",
" with gr.Row():\n",
" crypto_currency = gr.Dropdown([\"BTC\", \"ETH\", \"SOL\"], label=\"The Crypto cyrrency\")\n",
" wallet_balance = gr.Number(label=\"Enter a number\")\n",
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\", \"Llama\"], label=\"Select model\", value=\"GPT\")\n",
" with gr.Row():\n",
" generate_python_code_bt = gr.Button(\"Genarate Python code\")\n",
" with gr.Row():\n",
" with gr.Column():\n",
" python = gr.TextArea(label=\"Python Code\")\n",
" python_comment = gr.Button(\"Comment Python code\")\n",
" python_run = gr.Button(\"Run Python code\")\n",
" with gr.Row():\n",
" result_out = gr.TextArea(label=\"Trading advice\")\n",
"\n",
" generate_python_code_bt.click(generate_python_code, inputs=[model, crypto_currency, wallet_balance], outputs=[python])\n",
" python_comment.click(comment_python_code, inputs=[model, python], outputs=python)\n",
" python_run.click(execute_python, inputs=[python], outputs=result_out)\n",
"\n",
"ui.launch(inbrowser=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "",
"version": ""
}
},
"nbformat": 4,
"nbformat_minor": 5
}

288
week4/community-contributions/w4d3_unit_test.ipynb Normal file
View File

@@ -0,0 +1,288 @@
{
"cells": [
{
"cell_type": "markdown",
"id": "4a6ab9a2-28a2-445d-8512-a0dc8d1b54e9",
"metadata": {},
"source": [
"# Unit test Generator\n",
"\n",
"Create unit tests on the Python code"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e610bf56-a46e-4aff-8de1-ab49d62b1ad3",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"from dotenv import load_dotenv\n",
"from openai import OpenAI\n",
"from google import genai\n",
"from google.genai import types\n",
"import anthropic\n",
"import ollama\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f672e1c-87e9-4865-b760-370fa605e614",
"metadata": {},
"outputs": [],
"source": [
"# environment\n",
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['ANTHROPIC_API_KEY'] = os.getenv('ANTHROPIC_API_KEY', 'your-key-if-not-using-env')\n",
"os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your-key-if-not-using-env')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8aa149ed-9298-4d69-8fe2-8f5de0f667da",
"metadata": {},
"outputs": [],
"source": [
"# initialize\n",
"\n",
"openai = OpenAI()\n",
"claude = anthropic.Anthropic()\n",
"client = genai.Client()\n",
"\n",
"\n",
"OPENAI_MODEL = \"gpt-4o\"\n",
"CLAUDE_MODEL = \"claude-sonnet-4-20250514\"\n",
"GEMINI_MODEL = 'gemini-2.5-flash'\n",
"LLAMA_MODEL = \"llama3.2\"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6896636f-923e-4a2c-9d6c-fac07828a201",
"metadata": {},
"outputs": [],
"source": [
"system_message = \"\"\"\n",
"You are an effective programming assistant specialized to generate Python code based on the inputs.\n",
"Respond only with Python code; use comments sparingly and do not provide any explanation other than occasional comments.\n",
"Do not include Markdown formatting (```), language tags (python), or extra text \\n.\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8e7b3546-57aa-4c29-bc5d-f211970d04eb",
"metadata": {},
"outputs": [],
"source": [
"def user_prompt_for_unit_test(python):\n",
" user_prompt = f\"\"\"\n",
" Consider the following Python code: \\n\\n\n",
" {python} \\n\\n\n",
"\n",
" Generate a unit test around this code and it alongside with the Python code. \\n\n",
" Response rule: in your response do not include Markdown formatting (```), language tags (python), or extra text.\n",
"\n",
" \"\"\"\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c6190659-f54c-4951-bef4-4960f8e51cc4",
"metadata": {},
"outputs": [],
"source": [
"def messages_for_unit_test(python):\n",
" return [\n",
" {\"role\": \"system\", \"content\": system_message},\n",
" {\"role\": \"user\", \"content\": user_prompt_for_unit_test(python)}\n",
" ]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "c3b497b3-f569-420e-b92e-fb0f49957ce0",
"metadata": {},
"outputs": [],
"source": [
"python_hard = \"\"\"\n",
"\n",
"def lcg(seed, a=1664525, c=1013904223, m=2**32):\n",
" value = seed\n",
" while True:\n",
" value = (a * value + c) % m\n",
" yield value\n",
"\n",
"def max_subarray_sum(n, seed, min_val, max_val):\n",
" lcg_gen = lcg(seed)\n",
" random_numbers = [next(lcg_gen) % (max_val - min_val + 1) + min_val for _ in range(n)]\n",
" max_sum = float('-inf')\n",
" for i in range(n):\n",
" current_sum = 0\n",
" for j in range(i, n):\n",
" current_sum += random_numbers[j]\n",
" if current_sum > max_sum:\n",
" max_sum = current_sum\n",
" return max_sum\n",
"\n",
"def total_max_subarray_sum(n, initial_seed, min_val, max_val):\n",
" total_sum = 0\n",
" lcg_gen = lcg(initial_seed)\n",
" for _ in range(20):\n",
" seed = next(lcg_gen)\n",
" total_sum += max_subarray_sum(n, seed, min_val, max_val)\n",
" return total_sum\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0be9f47d-5213-4700-b0e2-d444c7c738c0",
"metadata": {},
"outputs": [],
"source": [
"def stream_gpt(python):\n",
" stream = openai.chat.completions.create(model=OPENAI_MODEL, messages=messages_for_unit_test(python), stream=True)\n",
" reply = \"\"\n",
" for chunk in stream:\n",
" fragment = chunk.choices[0].delta.content or \"\"\n",
" reply += fragment\n",
" yield reply"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "8669f56b-8314-4582-a167-78842caea131",
"metadata": {},
"outputs": [],
"source": [
"def stream_claude(python):\n",
" result = claude.messages.stream(\n",
" model=CLAUDE_MODEL,\n",
" max_tokens=2000,\n",
" system=system_message,\n",
" messages=[{\"role\": \"user\", \"content\": user_prompt_for_unit_test(python)}],\n",
" )\n",
" reply = \"\"\n",
" with result as stream:\n",
" for text in stream.text_stream:\n",
" reply += text\n",
" yield reply"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "97205162",
"metadata": {},
"outputs": [],
"source": [
"def stream_gemini(python):\n",
" response = client.models.generate_content_stream(\n",
" model=GEMINI_MODEL,\n",
" config=types.GenerateContentConfig(\n",
" system_instruction=system_message),\n",
" contents=user_prompt_for_unit_test(python)\n",
" )\n",
"\n",
" reply = \"\"\n",
" for chunk in response:\n",
" fragment = chunk.text or \"\"\n",
" reply += fragment\n",
" yield reply\n",
"\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4f94b13e",
"metadata": {},
"outputs": [],
"source": [
"def stream_llama_local(python):\n",
" stream = ollama.chat(\n",
" model='llama3.2',\n",
" messages=messages_for_unit_test(python),\n",
" stream=True,\n",
" )\n",
"\n",
" reply = \"\"\n",
" # Iterate through the streamed chunks and print the content\n",
" for chunk in stream:\n",
" #print(chunk['message']['content'], end='', flush=True)\n",
" if 'content' in chunk['message']:\n",
" fragment = chunk['message']['content']\n",
" reply += fragment\n",
" yield reply\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2f1ae8f5-16c8-40a0-aa18-63b617df078d",
"metadata": {},
"outputs": [],
"source": [
"def generate_unit_test(python, model):\n",
" if model==\"GPT\":\n",
" result = stream_gpt(python)\n",
" elif model==\"Claude\":\n",
" result = stream_claude(python)\n",
" elif model==\"Gemini\":\n",
" result = stream_gemini(python)\n",
" elif model==\"Llama\":\n",
" result = stream_llama_local(python)\n",
"\n",
" else:\n",
" raise ValueError(\"Unknown model\")\n",
" for stream_so_far in result:\n",
" yield stream_so_far"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f1ddb38e-6b0a-4c37-baa4-ace0b7de887a",
"metadata": {},
"outputs": [],
"source": [
"with gr.Blocks() as ui:\n",
" with gr.Row():\n",
" python = gr.Textbox(label=\"Python code:\", lines=10, value=python_hard)\n",
" unit_test = gr.Textbox(label=\"Unit test\", lines=10)\n",
" with gr.Row():\n",
" model = gr.Dropdown([\"GPT\", \"Claude\", \"Gemini\", \"Llama\"], label=\"Select model\", value=\"GPT\")\n",
" generate_ut = gr.Button(\"Generate Unit tests\")\n",
"\n",
" generate_ut.click(generate_unit_test, inputs=[python, model], outputs=[unit_test])\n",
"\n",
"ui.launch(inbrowser=True)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
}
},
"nbformat": 4,
"nbformat_minor": 5
}