{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Lab-2-Introduce Phi-3.5 Vision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from pathlib import Path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if not Path(\"ov_phi3_vision.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/phi-3-vision/ov_phi3_vision.py\")\n",
    "    open(\"ov_phi3_vision.py\", \"w\").write(r.text)\n",
    "\n",
    "\n",
    "if not Path(\"gradio_helper.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/notebooks/phi-3-vision/gradio_helper.py\")\n",
    "    open(\"gradio_helper.py\", \"w\").write(r.text)\n",
    "\n",
    "if not Path(\"notebook_utils.py\").exists():\n",
    "    r = requests.get(url=\"https://raw.githubusercontent.com/openvinotoolkit/openvino_notebooks/latest/utils/notebook_utils.py\")\n",
    "    open(\"notebook_utils.py\", \"w\").write(r.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ov_phi3_vision import convert_phi3_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import nncf\n",
    "\n",
    "\n",
    "model_id = \"microsoft/Phi-3.5-vision-instruct\"\n",
    "out_dir = Path(\"../model/phi-3.5-vision-128k-instruct-ov\")\n",
    "compression_configuration = {\n",
    "    \"mode\": nncf.CompressWeightsMode.INT4_SYM,\n",
    "    \"group_size\": 64,\n",
    "    \"ratio\": 0.6,\n",
    "}\n",
    "if not out_dir.exists():\n",
    "    convert_phi3_model(model_id, out_dir, compression_configuration)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from notebook_utils import device_widget\n",
    "\n",
    "device = device_widget(default=\"GPU\", exclude=[\"NPU\"])\n",
    "\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from ov_phi3_vision import OvPhi3Vision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "out_dir"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = OvPhi3Vision(out_dir, device.value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image = Image.open(r\"./imgs/demo.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from transformers import AutoProcessor, TextStreamer\n",
    "\n",
    "messages = [\n",
    "    {\"role\": \"user\", \"content\": \"<|image_1|>\\nPlease create Python code for image, and use plt to save the new picture under imgs/ and name it phi-3-vision.jpg.\"},\n",
    "]\n",
    "\n",
    "processor = AutoProcessor.from_pretrained(out_dir, trust_remote_code=True)\n",
    "\n",
    "prompt = processor.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)\n",
    "\n",
    "inputs = processor(prompt, [image], return_tensors=\"pt\")\n",
    "\n",
    "generation_args = {\"max_new_tokens\": 3072, \"do_sample\": False, \"streamer\": TextStreamer(processor.tokenizer, skip_prompt=True, skip_special_tokens=True)}\n",
    "\n",
    "print(\"Coding:\")\n",
    "generate_ids = model.generate(**inputs, eos_token_id=processor.tokenizer.eos_token_id, **generation_args)"
   ]
  }
 ],
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