FlaskApp.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w_W_nRW-AAD2",
        "outputId": "1414f8d9-5b6a-416f-97d8-7e8f2b56744f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: gdown in /usr/local/lib/python3.10/dist-packages (5.1.0)\n",
            "Requirement already satisfied: beautifulsoup4 in /usr/local/lib/python3.10/dist-packages (from gdown) (4.12.3)\n",
            "Requirement already satisfied: filelock in /usr/local/lib/python3.10/dist-packages (from gdown) (3.14.0)\n",
            "Requirement already satisfied: requests[socks] in /usr/local/lib/python3.10/dist-packages (from gdown) (2.31.0)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from gdown) (4.66.4)\n",
            "Requirement already satisfied: soupsieve>1.2 in /usr/local/lib/python3.10/dist-packages (from beautifulsoup4->gdown) (2.5)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (2024.2.2)\n",
            "Requirement already satisfied: PySocks!=1.5.7,>=1.5.6 in /usr/local/lib/python3.10/dist-packages (from requests[socks]->gdown) (1.7.1)\n"
          ]
        }
      ],
      "source": [
        "!pip install gdown"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 147
        },
        "id": "NTv4XgiKAPpa",
        "outputId": "aadf5773-9602-4398-853b-c660543d95a1"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1-Be04_a1hzkmuV6u2X3ZH9n6uXt72Qcj\n",
            "From (redirected): https://drive.google.com/uc?id=1-Be04_a1hzkmuV6u2X3ZH9n6uXt72Qcj&confirm=t&uuid=e35049a4-02c5-4ac5-a972-5b7e4d2be8ea\n",
            "To: /content/15000_last_checkpoint.pth\n",
            "100%|██████████| 507M/507M [00:03<00:00, 131MB/s]\n"
          ]
        },
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'15000_last_checkpoint.pth'"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "import gdown\n",
        "file_id = '1-Be04_a1hzkmuV6u2X3ZH9n6uXt72Qcj'\n",
        "destination = '15000_last_checkpoint.pth'\n",
        "gdown.download(f'https://drive.google.com/uc?id={file_id}', destination, quiet=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        },
        "id": "5cWfAd3yOfZa",
        "outputId": "a57a380f-a92c-4feb-8d1e-abaa1452b1e1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Downloading...\n",
            "From (original): https://drive.google.com/uc?id=1-3kloR13lQQJSMTP-B5nuIygnZG0uITW\n",
            "From (redirected): https://drive.google.com/uc?id=1-3kloR13lQQJSMTP-B5nuIygnZG0uITW&confirm=t&uuid=996ed1d6-d99d-4048-9bc4-6c90a52f0839\n",
            "To: /content/80000_last_checkpoint.pth\n",
            "100%|██████████| 507M/507M [00:06<00:00, 79.3MB/s]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'80000_last_checkpoint.pth'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "import gdown\n",
        "file_id2 = '1-3kloR13lQQJSMTP-B5nuIygnZG0uITW'\n",
        "destination = '80000_last_checkpoint.pth'\n",
        "gdown.download(f'https://drive.google.com/uc?id={file_id2}', destination, quiet=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YdZgdfv7Ug61",
        "outputId": "93d2220d-ae7d-4fe5-c84b-944a6e926549"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "config.json: 100% 665/665 [00:00<00:00, 3.18MB/s]\n",
            "model.safetensors: 100% 548M/548M [00:05<00:00, 96.5MB/s]\n",
            "generation_config.json: 100% 124/124 [00:00<00:00, 733kB/s]\n",
            "tokenizer_config.json: 100% 26.0/26.0 [00:00<00:00, 165kB/s]\n",
            "vocab.json: 100% 1.04M/1.04M [00:00<00:00, 2.01MB/s]\n",
            "merges.txt: 100% 456k/456k [00:00<00:00, 3.43MB/s]\n",
            "tokenizer.json: 100% 1.36M/1.36M [00:00<00:00, 4.17MB/s]\n",
            "100%|███████████████████████████████████████| 338M/338M [00:08<00:00, 43.2MiB/s]\n",
            "Traceback (most recent call last):\n",
            "  File \"/content/main.py\", line 7, in <module>\n",
            "    infer.query(\"/content/img.jpg\",\"what is child doing ?\")\n",
            "  File \"/content/inference.py\", line 20, in query\n",
            "    assert os.path.isfile(pathToImg) and type(textQuery) is str , \"Expected Image path and query in form of text\"\n",
            "AssertionError: Expected Image path and query in form of text\n"
          ]
        }
      ],
      "source": [
        "!python main.py"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "MMfAn2GEAaH0",
        "outputId": "dde7b999-20df-457a-a7d2-db0c7b52cc14"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "https://uynhws1pt4r-496ff2e9c6d22116-5000-colab.googleusercontent.com/\n"
          ]
        }
      ],
      "source": [
        "from google.colab.output import eval_js\n",
        "print(eval_js(\"google.colab.kernel.proxyPort(5000)\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QceYwy7sN2Dm",
        "outputId": "813bab7d-6fda-45ff-8899-d288617b8d42"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting openai-clip\n",
            "  Downloading openai-clip-1.0.1.tar.gz (1.4 MB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m10.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "Collecting ftfy (from openai-clip)\n",
            "  Downloading ftfy-6.2.0-py3-none-any.whl (54 kB)\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m54.4/54.4 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: regex in /usr/local/lib/python3.10/dist-packages (from openai-clip) (2023.12.25)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from openai-clip) (4.66.4)\n",
            "Requirement already satisfied: wcwidth<0.3.0,>=0.2.12 in /usr/local/lib/python3.10/dist-packages (from ftfy->openai-clip) (0.2.13)\n",
            "Building wheels for collected packages: openai-clip\n",
            "  Building wheel for openai-clip (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai-clip: filename=openai_clip-1.0.1-py3-none-any.whl size=1368605 sha256=8d9b7f2c953881858e35fe386389ed68219007aff73801d47ead637fca0685f6\n",
            "  Stored in directory: /root/.cache/pip/wheels/08/77/8e/8d2f862df6bf7fb4e2007062d2cbaeae49862ec7b56d041229\n",
            "Successfully built openai-clip\n",
            "Installing collected packages: ftfy, openai-clip\n",
            "Successfully installed ftfy-6.2.0 openai-clip-1.0.1\n"
          ]
        }
      ],
      "source": [
        "!pip install openai-clip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NwKIqfrnRZF_",
        "outputId": "d8565acb-3701-4b5e-bf05-5803057dc381"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
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        "colab": {
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        "id": "38eUEnvFBc1O",
        "outputId": "3b664480-f3bf-4618-d9f7-dc7720311d95"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/huggingface_hub/utils/_token.py:88: UserWarning: \n",
            "The secret `HF_TOKEN` does not exist in your Colab secrets.\n",
            "To authenticate with the Hugging Face Hub, create a token in your settings tab (https://huggingface.co/settings/tokens), set it as secret in your Google Colab and restart your session.\n",
            "You will be able to reuse this secret in all of your notebooks.\n",
            "Please note that authentication is recommended but still optional to access public models or datasets.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
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              "version_major": 2,
              "version_minor": 0,
              "model_id": "61310794a8e941b7a1fce79d9341d7d5"
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          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3221578484564d38831779b572ec3e0b"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0cc9969807d94a0bbb0918c4527090df"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "6eb29666ea69410fb4488b6eeaf482b4"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "0994f85f7aaa4597999797b5138a97ea"
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          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
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            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "2a91ef3f86954f59bd4dfa5b65729993"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|███████████████████████████████████████| 338M/338M [00:13<00:00, 26.9MiB/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " * Serving Flask app '__main__'\n",
            " * Debug mode: off\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:werkzeug:\u001b[31m\u001b[1mWARNING: This is a development server. Do not use it in a production deployment. Use a production WSGI server instead.\u001b[0m\n",
            " * Running on http://127.0.0.1:5000\n",
            "INFO:werkzeug:\u001b[33mPress CTRL+C to quit\u001b[0m\n"
          ]
        }
      ],
      "source": [
        "from flask import Flask, render_template, request, jsonify\n",
        "import os\n",
        "import importlib.util\n",
        "import sys\n",
        "import cv2\n",
        "\n",
        "# Specify the module name and file path\n",
        "module_name = \"inference\"\n",
        "file_path = \"/content/inference.py\"\n",
        "\n",
        "# Load the module\n",
        "spec = importlib.util.spec_from_file_location(module_name, file_path)\n",
        "module = importlib.util.module_from_spec(spec)\n",
        "sys.modules[module_name] = module\n",
        "spec.loader.exec_module(module)\n",
        "\n",
        "from inference import Inferece\n",
        "app = Flask(__name__, template_folder='/content/drive/MyDrive/templates')\n",
        "inference = Inferece('/content/80000_last_checkpoint.pth')\n",
        "\n",
        "def extract_frames_and_process(video_path, question):\n",
        "    frames_folder = \"/content/frames\"\n",
        "    extract_frames_at_intervals(video_path, frames_folder, 0.5)\n",
        "    answers = {}\n",
        "    for frame_file in sorted(os.listdir(frames_folder)):\n",
        "        frame_path = os.path.join(frames_folder, frame_file)\n",
        "        answer = inference.query(frame_path, question)\n",
        "        time_stamp = frame_file.split('_')[1].split('.')[0]\n",
        "        answers[time_stamp] = answer\n",
        "    return answers\n",
        "\n",
        "def extract_frames_at_intervals(video_path, output_folder, seconds_interval):\n",
        "    if not os.path.exists(output_folder):\n",
        "        os.makedirs(output_folder)\n",
        "    cap = cv2.VideoCapture(video_path)\n",
        "    if not cap.isOpened():\n",
        "        print(\"Error: Could not open video.\")\n",
        "        return\n",
        "    fps = cap.get(cv2.CAP_PROP_FPS)\n",
        "    frame_count = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n",
        "    duration = frame_count / fps\n",
        "    current_frame = 0\n",
        "    frame_interval = int(fps * seconds_interval)\n",
        "    while True:\n",
        "        ret, frame = cap.read()\n",
        "        if not ret:\n",
        "            break\n",
        "        if current_frame % frame_interval == 0:\n",
        "            output_file = os.path.join(output_folder, f\"frame_{current_frame:04d}.jpg\")\n",
        "            cv2.imwrite(output_file, frame)\n",
        "        current_frame += 1\n",
        "    cap.release()\n",
        "\n",
        "@app.route(\"/\", methods=[\"GET\", \"POST\"])\n",
        "def home():\n",
        "    if request.method == \"POST\":\n",
        "        if 'file' not in request.files or 'question' not in request.form:\n",
        "            return render_template('index.html', error=\"No file or question provided\"), 400\n",
        "        file = request.files['file']\n",
        "        question = request.form['question']\n",
        "        file_path = \"/content/tmp_file\"\n",
        "        file.save(file_path)\n",
        "        if file.filename.endswith('.mp4'):\n",
        "            answers = extract_frames_and_process(file_path, question)\n",
        "            os.remove(file_path)\n",
        "            return render_template('index.html', answers=answers)\n",
        "        else:\n",
        "            answer = inference.query(file_path, question)\n",
        "            os.remove(file_path)\n",
        "            return render_template('index.html', image_answer=answer)\n",
        "    return render_template('index.html')\n",
        "\n",
        "if __name__ == \"__main__\":\n",
        "    app.run()"
      ]
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