Merge pull request google-ai-edge#20 from googlesamples/feature/pytho…

…n_samples_for_preview Added Colab notebooks for Python demos.
arttupii · Dec 1, 2022 · 61411fd · 61411fd
2 parents 2574afe + 7e898ee
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diff --git a/examples/gesture_recognizer/python/gesture_recognizer.ipynb b/examples/gesture_recognizer/python/gesture_recognizer.ipynb
diff --git a/examples/hand_landmarker/python/hand_landmark.ipynb b/examples/hand_landmarker/python/hand_landmark.ipynb
diff --git a/examples/image_classification/python/image_classification.ipynb b/examples/image_classification/python/image_classification.ipynb
diff --git a/examples/object_detection/python/object_detection.ipynb b/examples/object_detection/python/object_detection.ipynb
diff --git a/examples/text_classification/python/text_classifier.ipynb b/examples/text_classification/python/text_classifier.ipynb
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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "h2q27gKz1H20"
+      },
+      "source": [
+        "##### Copyright 2022 The MediaPipe Authors. All Rights Reserved."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "cellView": "form",
+        "id": "TUfAcER1oUS6"
+      },
+      "outputs": [],
+      "source": [
+        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "L_cQX8dWu4Dv"
+      },
+      "source": [
+        "# Text Classifier with MediaPipe Tasks\n",
+        "\n",
+        "This notebook shows you how to use MediaPipe Tasks Python API to classify text. Check out the [MediaPipe documentation](https://developers.google.com/mediapipe/solutions/text/text_classifier/python) to learn more about configuration options that this solution supports."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "99IjoWCyDk7g"
+      },
+      "source": [
+        "## Preparation\n",
+        "\n",
+        "Let's start with installing MediaPipe."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "gxbHBsF-8Y_l"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -q flatbuffers==2.0.0\n",
+        "!pip install -q mediapipe==0.9.0"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "QGNTJpASRDpI"
+      },
+      "source": [
+        "Then download an off-the-shelf model. Check out the [MediaPipe documentation](https://developers.google.com/mediapipe/solutions/text/text_classifier#models) for more text classification models that you can use."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "OMjuVQiDYJKF"
+      },
+      "outputs": [],
+      "source": [
+        "!wget -O classifier.tflite -q https://storage.googleapis.com/mediapipe-tasks/text_classifier/bert_text_classifier.tflite "
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Iy4r2_ePylIa"
+      },
+      "source": [
+        "## Running inference\n",
+        "\n",
+        "Here are the steps to run text classification using MediaPipe:"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "Yl_Oiye4mUuo",
+        "outputId": "994627d4-56cd-40af-9f5d-abef38fa4592"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "positive (0.99)\n"
+          ]
+        }
+      ],
+      "source": [
+        "# STEP 1: Import the necessary modules.\n",
+        "from mediapipe.tasks import python\n",
+        "from mediapipe.tasks.python import text\n",
+        "\n",
+        "# STEP 2: Create an TextClassifier object.\n",
+        "base_options = python.BaseOptions(model_asset_path=\"classifier.tflite\")\n",
+        "options = text.TextClassifierOptions(base_options=base_options)\n",
+        "classifier = python.text.TextClassifier.create_from_options(options)\n",
+        "\n",
+        "# STEP 3: Classify the input text.\n",
+        "INPUT_TEXT = \"I'm looking forward to what will come next.\"\n",
+        "classification_result = classifier.classify(INPUT_TEXT)\n",
+        "\n",
+        "# STEP 4: Process the classification result. In this case, print out the most likely category.\n",
+        "top_category = classification_result.classifications[0].categories[0]\n",
+        "print(f'{top_category.category_name} ({top_category.score:.2f})')"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "id": "WPO6rvNJTkPd"
+      },
+      "outputs": [],
+      "source": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "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": "3.7.13"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}