GENAI_NOTEBOOKS/llama3-please-write-code-for-me.ipynb

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   "source": [
    "# Introduction\n",
    "\n",
    "\n",
    "We are using LLama3 to write code."
   ]
  },
  {
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   },
   "source": [
    "# Preparation"
   ]
  },
  {
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   "source": [
    "from time import time\n",
    "import torch\n",
    "import transformers\n",
    "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
    "from IPython.display import display, Markdown"
   ]
  },
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   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-04-21 20:41:03.124746: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-04-21 20:41:03.124847: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-04-21 20:41:03.239253: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
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     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "16828181d0d943ea9b7ef2a5f7c9d415",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    }
   ],
   "source": [
    "model = \"/kaggle/input/llama-3/transformers/8b-chat-hf/1\"\n",
    "\n",
    "pipeline = transformers.pipeline(\n",
    "    \"text-generation\",\n",
    "    model=model,\n",
    "    torch_dtype=torch.float16,\n",
    "    device_map=\"auto\",\n",
    ")"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "# Test the model\n",
    "\n",
    "Let's define the query model function."
   ]
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   "source": [
    "def query_model(\n",
    "    prompt, \n",
    "    temperature=0.2,\n",
    "    max_length=512\n",
    "    ):\n",
    "    time_start = time()\n",
    "    sequences = pipeline(\n",
    "        prompt,\n",
    "        do_sample=True,\n",
    "        top_k=10,\n",
    "        temperature=temperature,\n",
    "        num_return_sequences=1,\n",
    "        eos_token_id=pipeline.tokenizer.eos_token_id,\n",
    "        max_length=max_length,\n",
    "    )\n",
    "    time_end = time()\n",
    "    total_time = f\"{round(time_end-time_start, 3)} sec.\"\n",
    "    \n",
    "    question = sequences[0]['generated_text'][:len(prompt)]\n",
    "    answer = sequences[0]['generated_text'][len(prompt):]\n",
    "    \n",
    "    return f\"{question}\\n{answer}\\nTotal time: {total_time}\""
   ]
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   "source": [
    "We also define an utility function for displaying the results."
   ]
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   "source": [
    "def colorize_text(text):\n",
    "    for word, color in zip([\"Reasoning\", \"Question\", \"Answer\", \"Total time\"], [\"blue\", \"red\", \"green\", \"magenta\"]):\n",
    "        text = text.replace(f\"{word}:\", f\"\\n\\n**<font color='{color}'>{word}:</font>**\")\n",
    "    return text"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "Let's start testing the model."
   ]
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     "text": [
      "Truncation was not explicitly activated but `max_length` is provided a specific value, please use `truncation=True` to explicitly truncate examples to max length. Defaulting to 'longest_first' truncation strategy. If you encode pairs of sequences (GLUE-style) with the tokenizer you can select this strategy more precisely by providing a specific strategy to `truncation`.\n",
      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
     ]
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     "data": {
      "text/markdown": [
       "\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to calculate the area of a rectangle with edges of L and l\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "\n",
       "```\n",
       "def rectangle_area(L, l):\n",
       "    return L * l\n",
       "```````\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to calculate the area of a rectangle with edges of L and l\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def rectangle_area(L, l):\n",
       "    return L * l\n",
       "```````\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to calculate the area of a circle with radius r\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def circle_area(r):\n",
       "    return 3.14 * (r ** 2)\n",
       "```````\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to calculate the area of a triangle with base b and height h\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def triangle_area(b, h):\n",
       "    return 0.5\n",
       "\n",
       "\n",
       "**<font color='magenta'>Total time:</font>** 16.569 sec."
      ],
      "text/plain": [
       "<IPython.core.display.Markdown object>"
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   "source": [
    "prompt = \"\"\"\n",
    "You are an AI assistant designed to write simple Python code.\n",
    "Please answer with the listing of the Python code.\n",
    "Question: {question}\n",
    "Answer:\n",
    "\"\"\"\n",
    "response = query_model(\n",
    "    prompt.format(question=\"Please write a function in Python to calculate the area of a rectangle with edges of L and l\"),\n",
    "    max_length=256)\n",
    "display(Markdown(colorize_text(response)))"
   ]
  },
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      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
     ]
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     "data": {
      "text/markdown": [
       "\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to order a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "\n",
       "```\n",
       "def order_list(lst):\n",
       "    return sorted(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to reverse a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def reverse_list(lst):\n",
       "    return lst[::-1]\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the maximum value in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def max_value(lst):\n",
       "    return max(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the minimum value in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def min_value(lst):\n",
       "    return min(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the sum of all elements in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def sum_list(lst):\n",
       "    return sum(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the average of all elements in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def average_list(lst):\n",
       "    return sum(lst) / len(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the count of a specific element in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def count_element(lst, element):\n",
       "    return lst.count(element)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the index of a specific element in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def find_index(lst, element):\n",
       "    return lst.index(element)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to remove a specific element from a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def remove_element(lst, element):\n",
       "    return [i for i in lst if i!= element]\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to remove duplicates from a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def remove_duplicates(lst):\n",
       "    return list(set(lst))\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to sort a list in descending order\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def sort_descending(lst):\n",
       "    return sorted(lst, reverse=True)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to sort a list in ascending order\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def sort_ascending(lst):\n",
       "    return sorted(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the length of a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def length_list(lst):\n",
       "    return len(lst)\n",
       "```\n",
       "\n",
       "\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a function in Python to find the index of the first occurrence of a specific element in a list\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "```\n",
       "def find_first_index(lst, element):\n",
       "\n",
       "\n",
       "\n",
       "**<font color='magenta'>Total time:</font>** 32.407 sec."
      ],
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    "prompt = \"\"\"\n",
    "You are an AI assistant designed to write simple Python code.\n",
    "Please answer with the listing of the Python code.\n",
    "Question: {question}\n",
    "Answer:\n",
    "\"\"\"\n",
    "response = query_model(\n",
    "    prompt.format(question=\"Please write a function in Python to order a list\"), \n",
    "    max_length=512)\n",
    "display(Markdown(colorize_text(response)))"
   ]
  },
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      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
     ]
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     "data": {
      "text/markdown": [
       "\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** Please write a Python module for a phone agenda.\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "\n",
       "```python\n",
       "class PhoneAgenda:\n",
       "    def __init__(self):\n",
       "        self.contacts = {}\n",
       "\n",
       "    def add_contact(self, name, phone_number):\n",
       "        self.contacts[name] = phone_number\n",
       "\n",
       "    def delete_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            del self.contacts[name]\n",
       "        else:\n",
       "            print(\"Contact not found\")\n",
       "\n",
       "    def search_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            return self.contacts[name]\n",
       "        else:\n",
       "            return None\n",
       "\n",
       "    def display_contacts(self):\n",
       "        for name, phone_number in self.contacts.items():\n",
       "            print(f\"{name}: {phone_number}\")\n",
       "\n",
       "# Example usage:\n",
       "agenda = PhoneAgenda()\n",
       "agenda.add_contact(\"John Doe\", \"123-456-7890\")\n",
       "agenda.add_contact(\"Jane Smith\", \"098-765-4321\")\n",
       "agenda.display_contacts()\n",
       "print(agenda.search_contact(\"John Doe\"))\n",
       "agenda.delete_contact(\"Jane Smith\")\n",
       "agenda.display_contacts()\n",
       "```\n",
       "This Python module defines a `PhoneAgenda` class that allows you to add, delete, and search for contacts in a phone agenda. The `add_contact` method adds a new contact to the agenda, the `delete_contact` method removes a contact from the agenda, the `search_contact` method returns the phone number of a contact if it exists, and the `display_contacts` method prints out all the contacts in the agenda. The example usage at the end demonstrates how to use the `PhoneAgenda` class.assistant\n",
       "\n",
       "Here is the Python code for a phone agenda:\n",
       "```\n",
       "class PhoneAgenda:\n",
       "    def __init__(self):\n",
       "        self.contacts = {}\n",
       "\n",
       "    def add_contact(self, name, phone_number):\n",
       "        self.contacts[name] = phone_number\n",
       "\n",
       "    def delete_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            del self.contacts[name]\n",
       "        else:\n",
       "            print(\"Contact not found\")\n",
       "\n",
       "    def search_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            return self.contacts[name]\n",
       "        else:\n",
       "            return None\n",
       "\n",
       "    def display_contacts(self):\n",
       "        for name, phone_number in self.contacts.items():\n",
       "            print(f\"{name}: {phone_number}\")\n",
       "\n",
       "# Example usage:\n",
       "agenda = PhoneAgenda()\n",
       "agenda.add_contact(\"John Doe\", \"123-456-7890\")\n",
       "agenda.add_contact(\"Jane Smith\", \"098-765-4321\")\n",
       "agenda.display_contacts()\n",
       "print(agenda.search_contact(\"John Doe\"))\n",
       "agenda.delete_contact(\"Jane Smith\")\n",
       "agenda.display_contacts()\n",
       "```assistant\n",
       "\n",
       "Here is the Python code for a phone agenda:\n",
       "```\n",
       "class PhoneAgenda:\n",
       "    def __init__(self):\n",
       "        self.contacts = {}\n",
       "\n",
       "    def add_contact(self, name, phone_number):\n",
       "        self.contacts[name] = phone_number\n",
       "\n",
       "    def delete_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            del self.contacts[name]\n",
       "        else:\n",
       "            print(\"Contact not found\")\n",
       "\n",
       "    def search_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            return self.contacts[name]\n",
       "        else:\n",
       "            return None\n",
       "\n",
       "    def display_contacts(self):\n",
       "        for name, phone_number in self.contacts.items():\n",
       "            print(f\"{name}: {phone_number}\")\n",
       "\n",
       "# Example usage:\n",
       "agenda = PhoneAgenda()\n",
       "agenda.add_contact(\"John Doe\", \"123-456-7890\")\n",
       "agenda.add_contact(\"Jane Smith\", \"098-765-4321\")\n",
       "agenda.display_contacts()\n",
       "print(agenda.search_contact(\"John Doe\"))\n",
       "agenda.delete_contact(\"Jane Smith\")\n",
       "agenda.display_contacts()\n",
       "```assistant\n",
       "\n",
       "Here is the Python code for a phone agenda:\n",
       "```\n",
       "class PhoneAgenda:\n",
       "    def __init__(self):\n",
       "        self.contacts = {}\n",
       "\n",
       "    def add_contact(self, name, phone_number):\n",
       "        self.contacts[name] = phone_number\n",
       "\n",
       "    def delete_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            del self.contacts[name]\n",
       "        else:\n",
       "            print(\"Contact not found\")\n",
       "\n",
       "    def search_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            return self.contacts[name]\n",
       "        else:\n",
       "            return None\n",
       "\n",
       "    def display_contacts(self):\n",
       "        for name, phone_number in self.contacts.items():\n",
       "            print(f\"{name}: {phone_number}\")\n",
       "\n",
       "# Example usage:\n",
       "agenda = PhoneAgenda()\n",
       "agenda.add_contact(\"John Doe\", \"123-456-7890\")\n",
       "agenda.add_contact(\"Jane Smith\", \"098-765-4321\")\n",
       "agenda.display_contacts()\n",
       "print(agenda.search_contact(\"John Doe\"))\n",
       "agenda.delete_contact(\"Jane Smith\")\n",
       "agenda.display_contacts()\n",
       "```assistant\n",
       "\n",
       "Here is the Python code for a phone agenda:\n",
       "```\n",
       "class PhoneAgenda:\n",
       "    def __init__(self):\n",
       "        self.contacts = {}\n",
       "\n",
       "    def add_contact(self, name, phone_number):\n",
       "        self.contacts[name] = phone_number\n",
       "\n",
       "    def delete_contact(self, name):\n",
       "        if name in self.contacts:\n",
       "            del self.contacts[name]\n",
       "        else:\n",
       "\n",
       "\n",
       "\n",
       "**<font color='magenta'>Total time:</font>** 68.931 sec."
      ],
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    "Question: {question}\n",
    "Answer:\n",
    "\"\"\"\n",
    "response = query_model(\n",
    "    prompt.format(question=\"Please write a Python module for a phone agenda.\"), \n",
    "    max_length=1024)\n",
    "display(Markdown(colorize_text(response)))"
   ]
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     "text": [
      "Setting `pad_token_id` to `eos_token_id`:128001 for open-end generation.\n"
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       "\n",
       "You are an AI assistant designed to write simple Python code.\n",
       "Please answer with the listing of the Python code.\n",
       "\n",
       "\n",
       "**<font color='red'>Question:</font>** \n",
       "                            Please write a Python service using Flask to expose a machine learning model. \n",
       "                            The service has one endpoint (POST) that receives three features:\n",
       "                            - area\n",
       "                            - number of rooms\n",
       "                            - number of bathrooms\n",
       "                            The model returns the price of the property.\n",
       "\n",
       "\n",
       "**<font color='green'>Answer:</font>**\n",
       "\n",
       "Here is the Python code for a Flask service that exposes a machine learning model:\n",
       "```\n",
       "from flask import Flask, request, jsonify\n",
       "import pandas as pd\n",
       "from sklearn.ensemble import RandomForestRegressor\n",
       "from sklearn.preprocessing import StandardScaler\n",
       "\n",
       "app = Flask(__name__)\n",
       "\n",
       "# Load the machine learning model\n",
       "model = RandomForestRegressor()\n",
       "model.load(\"model.pkl\")\n",
       "\n",
       "# Load the feature scaling\n",
       "scaler = StandardScaler()\n",
       "scaler.load(\"scaler.pkl\")\n",
       "\n",
       "@app.route('/predict', methods=['POST'])\n",
       "def predict():\n",
       "    data = request.get_json()\n",
       "    area = data['area']\n",
       "    num_rooms = data['number_of_rooms']\n",
       "    num_bathrooms = data['number_of_bathrooms']\n",
       "\n",
       "    # Scale the input features\n",
       "    scaled_features = scaler.transform([[area, num_rooms, num_bathrooms]])\n",
       "\n",
       "    # Make a prediction using the machine learning model\n",
       "    prediction = model.predict(scaled_features)[0]\n",
       "\n",
       "    return jsonify({'price': prediction})\n",
       "\n",
       "if __name__ == '__main__':\n",
       "    app.run(debug=True)\n",
       "```\n",
       "This code assumes that you have a machine learning model and feature scaling saved as `model.pkl` and `scaler.pkl` files, respectively. You'll need to replace these with your own model and scaling files.\n",
       "\n",
       "The service listens for POST requests to the `/predict` endpoint, which receives a JSON object with the three features: `area`, `number_of_rooms`, and `number_of_bathrooms`. The service scales these features using the `StandardScaler`, makes a prediction using the machine learning model, and returns the predicted price as a JSON response.assistant:\n",
       "\n",
       "Here is the Python code for a Flask service that exposes a machine learning model:\n",
       "```\n",
       "from flask import Flask, request, jsonify\n",
       "import pandas as pd\n",
       "from sklearn.ensemble import RandomForestRegressor\n",
       "from sklearn.preprocessing import StandardScaler\n",
       "\n",
       "app = Flask(__name__)\n",
       "\n",
       "# Load the machine learning model\n",
       "model = RandomForestRegressor()\n",
       "model.load(\"model.pkl\")\n",
       "\n",
       "# Load the feature scaling\n",
       "scaler = StandardScaler()\n",
       "scaler.load(\"scaler.pkl\")\n",
       "\n",
       "@app.route('/predict', methods=['POST\n",
       "\n",
       "\n",
       "**<font color='magenta'>Total time:</font>** 29.453 sec."
      ],
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    "    prompt.format(question=\"\"\"\n",
    "                            Please write a Python service using Flask to expose a machine learning model. \n",
    "                            The service has one endpoint (POST) that receives three features:\n",
    "                            - area\n",
    "                            - number of rooms\n",
    "                            - number of bathrooms\n",
    "                            The model returns the price of the property.\"\"\"), \n",
    "    max_length=512)\n",
    "display(Markdown(colorize_text(response)))"
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