C rx v1

App.js

@@ -6,109 +6,34 @@
  * @flow strict-local
  */
 
-import React from 'react';
-import {
-  SafeAreaView,
-  StyleSheet,
-  ScrollView,
-  View,
-  Text,
-  StatusBar,
-} from 'react-native';
+import React, {useState, useEffect} from 'react';
+import {View, Text} from 'react-native';
+import auth from '@react-native-firebase/auth';
+import LoginScreen from './screens/Loginscreen';
+import NavStack from './NavStack';
 
-import {
-  Header,
-  LearnMoreLinks,
-  Colors,
-  DebugInstructions,
-  ReloadInstructions,
-} from 'react-native/Libraries/NewAppScreen';
+export default function App() {
+  // Set an initializing state whilst Firebase connects
+  const [initializing, setInitializing] = useState(true);
+  const [user, setUser] = useState();
 
-const App: () => React$Node = () => {
-  return (
-    <>
-      <StatusBar barStyle="dark-content" />
-      <SafeAreaView>
-        <ScrollView
-          contentInsetAdjustmentBehavior="automatic"
-          style={styles.scrollView}>
-          <Header />
-          {global.HermesInternal == null ? null : (
-            <View style={styles.engine}>
-              <Text style={styles.footer}>Engine: Hermes</Text>
-            </View>
-          )}
-          <View style={styles.body}>
-            <View style={styles.sectionContainer}>
-              <Text style={styles.sectionTitle}>Step One</Text>
-              <Text style={styles.sectionDescription}>
-                Edit <Text style={styles.highlight}>App.js</Text> to change this
-                screen and then come back to see your edits.
-              </Text>
-            </View>
-            <View style={styles.sectionContainer}>
-              <Text style={styles.sectionTitle}>See Your Changes</Text>
-              <Text style={styles.sectionDescription}>
-                <ReloadInstructions />
-              </Text>
-            </View>
-            <View style={styles.sectionContainer}>
-              <Text style={styles.sectionTitle}>Debug</Text>
-              <Text style={styles.sectionDescription}>
-                <DebugInstructions />
-              </Text>
-            </View>
-            <View style={styles.sectionContainer}>
-              <Text style={styles.sectionTitle}>Learn More</Text>
-              <Text style={styles.sectionDescription}>
-                Read the docs to discover what to do next:
-              </Text>
-            </View>
-            <LearnMoreLinks />
-          </View>
-        </ScrollView>
-      </SafeAreaView>
-    </>
-  );
-};
+  // Handle user state changes
+  function onAuthStateChanged(user) {
+    setUser(user);
+    console.log(user);
+    if (initializing) setInitializing(false);
+  }
 
-const styles = StyleSheet.create({
-  scrollView: {
-    backgroundColor: Colors.lighter,
-  },
-  engine: {
-    position: 'absolute',
-    right: 0,
-  },
-  body: {
-    backgroundColor: Colors.white,
-  },
-  sectionContainer: {
-    marginTop: 32,
-    paddingHorizontal: 24,
-  },
-  sectionTitle: {
-    fontSize: 24,
-    fontWeight: '600',
-    color: Colors.black,
-  },
-  sectionDescription: {
-    marginTop: 8,
-    fontSize: 18,
-    fontWeight: '400',
-    color: Colors.dark,
-  },
-  highlight: {
-    fontWeight: '700',
-  },
-  footer: {
-    color: Colors.dark,
-    fontSize: 12,
-    fontWeight: '600',
-    padding: 4,
-    paddingRight: 12,
-    textAlign: 'right',
-  },
-});
+  useEffect(() => {
+    const subscriber = auth().onAuthStateChanged(onAuthStateChanged);
+    return subscriber; // unsubscribe on unmount
+  }, []);
 
-export default App;
+  if (initializing) return null;
+
+  if (!user) {
+    return <LoginScreen />;
+  }
+
+  return <NavStack />;
+}

BUGKILL.ipynb

@@ -0,0 +1,248 @@
+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "BUGKILL.ipynb",
+      "provenance": [],
+      "private_outputs": true,
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Xp6R2O-y8Sen",
+        "colab_type": "text"
+      },
+      "source": [
+        ""
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "kfGnaJpkVfSo",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "\n",
+        "#To use GPU FAISS use\n",
+        "!wget  https://anaconda.org/pytorch/faiss-gpu/1.2.1/download/linux-64/faiss-gpu-1.2.1-py36_cuda9.0.176_1.tar.bz2\n",
+        "!tar xvjf /content/faiss-gpu-1.2.1-py36_cuda9.0.176_1.tar.bz2\n",
+        "!cp -r lib/python3.6/site-packages/* /usr/local/lib/python3.6/dist-packages/\n",
+        "!pip install mkl"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "LzhkYUPoVr2k",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!python3 -c 'import tensorflow as tf; print(tf.__version__)'"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nrT9ywnWVjx1",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!pip uninstall tensorflow"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "BtjT3Td-bj_x",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!pip install tensorflow==2.0.0-alpha0"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "fRKhQ1f5Vl4i",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        "!pip install https://github.com/re-search/DocProduct/archive/v0.2.0_dev.zip\n",
+        "!pip install gpt2-estimator\n",
+        "!pip install pyarrow"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "2WSlwhdFC_0K",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "source": [
+        "\n",
+        "\n",
+        "def download_file_from_google_drive(id, destination):\n",
+        "    URL = \"https://docs.google.com/uc?export=download\"\n",
+        "\n",
+        "    session = requests.Session()\n",
+        "\n",
+        "    response = session.get(URL, params = { 'id' : id }, stream = True)\n",
+        "    token = get_confirm_token(response)\n",
+        "\n",
+        "    if token:\n",
+        "        params = { 'id' : id, 'confirm' : token }\n",
+        "        response = session.get(URL, params = params, stream = True)\n",
+        "\n",
+        "    save_response_content(response, destination)    \n",
+        "\n",
+        "def get_confirm_token(response):\n",
+        "    for key, value in response.cookies.items():\n",
+        "        if key.startswith('download_warning'):\n",
+        "            return value\n",
+        "\n",
+        "    return None\n",
+        "\n",
+        "def save_response_content(response, destination):\n",
+        "    CHUNK_SIZE = 32768\n",
+        "\n",
+        "    with open(destination, \"wb\") as f:\n",
+        "        for chunk in response.iter_content(CHUNK_SIZE):\n",
+        "            if chunk: # filter out keep-alive new chunks\n",
+        "                f.write(chunk)\n",
+        "                \n",
+        "import os\n",
+        "import requests\n",
+        "\n",
+        "import urllib.request\n",
+        "\n",
+        "# Download the file from `url` and save it locally under `file_name`:\n",
+        "urllib.request.urlretrieve('https://github.com/naver/biobert-pretrained/releases/download/v1.0-pubmed-pmc/biobert_v1.0_pubmed_pmc.tar.gz', 'BioBert.tar.gz')\n",
+        "\n",
+        "if not os.path.exists('BioBertFolder'):\n",
+        "    os.makedirs('BioBertFolder')\n",
+        "    \n",
+        "import tarfile\n",
+        "tar = tarfile.open(\"BioBert.tar.gz\")\n",
+        "tar.extractall(path='BioBertFolder/')\n",
+        "tar.close()\n",
+        "\n",
+        "file_id = '1uCXv6mQkFfpw5txGnVCsl93Db7t5Z2mp'\n",
+        "\n",
+        "download_file_from_google_drive(file_id, 'Float16EmbeddingsExpanded5-27-19.pkl')\n",
+        "\n",
+        "file_id = 'https://onedrive.live.com/download?cid=9DEDF3C1E2D7E77F&resid=9DEDF3C1E2D7E77F%2132792&authkey=AEQ8GtkcDbe3K98'\n",
+        "    \n",
+        "urllib.request.urlretrieve( file_id, 'DataAndCheckpoint.zip')\n",
+        "\n",
+        "if not os.path.exists('newFolder'):\n",
+        "    os.makedirs('newFolder')\n",
+        "\n",
+        "import zipfile\n",
+        "zip_ref = zipfile.ZipFile('DataAndCheckpoint.zip', 'r')\n",
+        "zip_ref.extractall('newFolder')\n",
+        "zip_ref.close()"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "ztuTHeW4jFiH",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "source": [
+        "# Load model weights and Q&A data. Double click to see code\n",
+        "\n",
+        "from docproduct.predictor import RetreiveQADoc\n",
+        "\n",
+        "pretrained_path = 'BioBertFolder/biobert_v1.0_pubmed_pmc/'\n",
+        "# ffn_weight_file = None\n",
+        "bert_ffn_weight_file = 'newFolder/models/bertffn_crossentropy/bertffn'\n",
+        "embedding_file = 'Float16EmbeddingsExpanded5-27-19.pkl'\n",
+        "\n",
+        "doc = RetreiveQADoc(pretrained_path=pretrained_path,\n",
+        "ffn_weight_file=None,\n",
+        "bert_ffn_weight_file=bert_ffn_weight_file,\n",
+        "embedding_file=embedding_file)"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "qZ110ZqNfBac",
+        "colab_type": "code",
+        "cellView": "both",
+        "colab": {}
+      },
+      "source": [
+        "#@title Type in your question (512 word limit) and search search parameters\n",
+        "\n",
+        "question_text = \"i am 18 and having frequent and painful urination, with a mild fever since 2-3 days\"  #@param {type:\"string\"}\n",
+        "\n",
+        "search_similarity_by = 'answer'  #@param ['answer', \"question\"]\n",
+        "\n",
+        "number_results_toReturn=10 #@param {type:\"number\"}\n",
+        "\n",
+        "answer_only=True #@param [\"False\", \"True\"] {type:\"raw\"}\n",
+        "\n",
+        "returned_results = doc.predict( question_text ,\n",
+        "                  search_by=search_similarity_by, topk=number_results_toReturn, answer_only=answer_only)\n",
+        "\n",
+        "print('')\n",
+        "anitr = 0\n",
+        "answer_array = []\n",
+        "for jk in range(len(returned_results)):\n",
+        "    if (len(returned_results[jk])>300):\n",
+        "      answer_array.append(returned_results[jk])\n",
+        "print(answer_array[anitr])\n"
+      ],
+      "execution_count": 0,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "nU42uyTG_oZr",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}