Merge pull request #3 from nickwalton/refactor

Refactor

.gitignore

@@ -1,3 +1,3 @@
 **/__pychache__
 .idea
-RL
+*.json

AI-Adventure-2bb65e3a4e2f.json

@@ -1,12 +0,0 @@
-{
-  "type": "service_account",
-  "project_id": "ai-adventure",
-  "private_key_id": "2bb65e3a4e2f0c0ef073cb0f10c9c26e1bb1447b",
-  "private_key": "-----BEGIN PRIVATE KEY-----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\nnVir78ePOnyRJzLKm2iql1I=\n-----END PRIVATE KEY-----\n",
-  "client_email": "ai-adventure@appspot.gserviceaccount.com",
-  "client_id": "100390463556501175877",
-  "auth_uri": "https://accounts.google.com/o/oauth2/auth",
-  "token_uri": "https://oauth2.googleapis.com/token",
-  "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
-  "client_x509_cert_url": "https://www.googleapis.com/robot/v1/metadata/x509/ai-adventure%40appspot.gserviceaccount.com"
-}

README.md

@@ -1 +1 @@
-# Keys
+#AIDungeon Code

console_play.py

@@ -0,0 +1,80 @@
+import os
+from story.utils import *
+from google.cloud import storage
+import json
+from story.story_manager import *
+from generator.web.web_generator import *
+import tensorflow as tf
+import textwrap
+
+
+# Set the key
+def console_print(str):
+    LINE_WIDTH=80
+    print((textwrap.fill(str, 80)))
+
+
+def play_unconstrained():
+    generator = WebGenerator("./AI-Adventure-2bb65e3a4e2f.json")
+    prompt = "You enter a dungeon with your trusty sword and shield. You are searching for the evil necromancer who killed your family. You've heard that he resides at the bottom of the dungeon, guarded by legions of the undead. You enter the first door and see"
+    story_manager = UnconstrainedStoryManager(generator, prompt)
+
+    console_print(str(story_manager.story))
+    while (True):
+        action = input("> ")
+        action = "You " + action
+        result = story_manager.act(action)
+        console_print(action + result)
+
+    #
+    #
+    #
+    # def act(self, action_choice):
+    #
+    #     action, result = self.possible_action_results[action_choice]
+    #     self.story.add_to_story(action, result)
+    #     self.possible_action_results = self.get_action_results()
+    #     return result, self.possible_action_results
+    #
+    # def story_context(self):
+    #     return self.story.latest_result()
+    #
+    # def get_action_results(self):
+    #     return [self.generate_action_result(self.story_context(), phrase) for phrase in self.action_phrases]
+    #
+    # def generate_action_result(self, prompt, phrase):
+    #     action = phrase + self.generator.generate(prompt + phrase)
+    #     action_result = cut_trailing_sentence(action)
+    #
+    #     action, result = split_first_sentence(action_result)
+    #     result = story_replace(action_result)
+    #     action = action_replace(action)
+    #
+    #     return action, result
+
+def play_constrained():
+    generator = WebGenerator("./AI-Adventure-2bb65e3a4e2f.json")
+    prompt = "You enter a dungeon with your trusty sword and shield. You are searching for the evil necromancer who killed your family. You've heard that he resides at the bottom of the dungeon, guarded by legions of the undead. You enter the first door and see"
+    story_manager = ConstrainedStoryManager(generator, prompt)
+
+    console_print(str(story_manager.story))
+    possible_actions = story_manager.get_possible_actions()
+    while (True):
+        console_print("\nOptions:")
+        for i, action in enumerate(possible_actions):
+            console_print(str(i) + ") " + action)
+
+        result = None
+        while(result == None):
+            action_choice = input("Which action do you choose? ")
+            print("\n")
+            result, possible_actions = story_manager.act(action_choice)
+
+        console_print(result)
+
+
+if __name__ == '__main__':
+    play_constrained()
+
+
+

cortex.sh

@@ -1,468 +0,0 @@
-#!/bin/bash
-
-# Copyright 2019 Cortex Labs, Inc.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-set -e
-
-####################
-### FLAG PARSING ###
-####################
-
-flag_help=false
-positional_args=()
-
-while [[ $# -gt 0 ]]; do
-  key="$1"
-  case $key in
-    -c|--config)
-    export CORTEX_CONFIG="$2"
-    shift
-    shift
-    ;;
-    -h|--help)
-    flag_help="true"
-    shift
-    ;;
-    *)
-    positional_args+=("$1")
-    shift
-    ;;
-  esac
-done
-
-set -- "${positional_args[@]}"
-positional_args=()
-for i in "$@"; do
-  case $i in
-    -c=*|--config=*)
-    export CORTEX_CONFIG="${i#*=}"
-    shift
-    ;;
-    -h=*|--help=*)
-    flag_help="true"
-    ;;
-    *)
-    positional_args+=("$1")
-    shift
-    ;;
-  esac
-done
-
-set -- "${positional_args[@]}"
-if [ "$flag_help" == "true" ]; then
-  show_help
-  exit 0
-fi
-
-for arg in "$@"; do
-  if [[ "$arg" == -* ]]; then
-    echo "unknown flag: $arg"
-    show_help
-    exit 1
-  fi
-done
-
-#####################
-### CONFIGURATION ###
-#####################
-
-if [ "$CORTEX_CONFIG" != "" ]; then
-  if [ ! -f "$CORTEX_CONFIG" ]; then
-    echo "Cortex config file does not exist: $CORTEX_CONFIG"
-    exit 1
-  fi
-  source $CORTEX_CONFIG
-fi
-
-set -u
-
-export CORTEX_VERSION_STABLE=0.7.3
-
-# Defaults
-export AWS_ACCESS_KEY_ID="${AWS_ACCESS_KEY_ID:-""}"
-
-if [ "$AWS_ACCESS_KEY_ID" = "" ]; then
-  echo -e "\nPlease set AWS_ACCESS_KEY_ID"
-  exit 1
-fi
-
-export AWS_SECRET_ACCESS_KEY="${AWS_SECRET_ACCESS_KEY:-""}"
-
-if [ "$AWS_SECRET_ACCESS_KEY" = "" ]; then
-  echo -e "\nPlease set AWS_SECRET_ACCESS_KEY"
-  exit 1
-fi
-
-export CORTEX_LOG_GROUP="${CORTEX_LOG_GROUP:-cortex}"
-export CORTEX_BUCKET="${CORTEX_BUCKET:-""}"
-export CORTEX_REGION="${CORTEX_REGION:-us-west-2}"
-export CORTEX_ZONES="${CORTEX_ZONES:-""}"
-
-export CORTEX_CLUSTER="${CORTEX_CLUSTER:-cortex}"
-export CORTEX_NODE_TYPE="${CORTEX_NODE_TYPE:-t3.large}"
-export CORTEX_NODES_MIN="${CORTEX_NODES_MIN:-2}"
-export CORTEX_NODES_MAX="${CORTEX_NODES_MAX:-5}"
-export CORTEX_NAMESPACE="${CORTEX_NAMESPACE:-cortex}"
-
-export CORTEX_IMAGE_MANAGER="${CORTEX_IMAGE_MANAGER:-cortexlabs/manager:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_FLUENTD="${CORTEX_IMAGE_FLUENTD:-cortexlabs/fluentd:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_NGINX_BACKEND="${CORTEX_IMAGE_NGINX_BACKEND:-cortexlabs/nginx-backend:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_NGINX_CONTROLLER="${CORTEX_IMAGE_NGINX_CONTROLLER:-cortexlabs/nginx-controller:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_OPERATOR="${CORTEX_IMAGE_OPERATOR:-cortexlabs/operator:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_SPARK="${CORTEX_IMAGE_SPARK:-cortexlabs/spark:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_SPARK_OPERATOR="${CORTEX_IMAGE_SPARK_OPERATOR:-cortexlabs/spark-operator:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_TF_SERVE="${CORTEX_IMAGE_TF_SERVE:-cortexlabs/tf-serve:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_TF_TRAIN="${CORTEX_IMAGE_TF_TRAIN:-cortexlabs/tf-train:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_TF_API="${CORTEX_IMAGE_TF_API:-cortexlabs/tf-api:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_PYTHON_PACKAGER="${CORTEX_IMAGE_PYTHON_PACKAGER:-cortexlabs/python-packager:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_TF_SERVE_GPU="${CORTEX_IMAGE_TF_SERVE_GPU:-cortexlabs/tf-serve-gpu:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_TF_TRAIN_GPU="${CORTEX_IMAGE_TF_TRAIN_GPU:-cortexlabs/tf-train-gpu:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_ONNX_SERVE="${CORTEX_IMAGE_ONNX_SERVE:-cortexlabs/onnx-serve:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_ONNX_SERVE_GPU="${CORTEX_IMAGE_ONNX_SERVE_GPU:-cortexlabs/onnx-serve-gpu:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_CLUSTER_AUTOSCALER="${CORTEX_IMAGE_CLUSTER_AUTOSCALER:-cortexlabs/cluster-autoscaler:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_NVIDIA="${CORTEX_IMAGE_NVIDIA:-cortexlabs/nvidia:$CORTEX_VERSION_STABLE}"
-export CORTEX_IMAGE_METRICS_SERVER="${CORTEX_IMAGE_METRICS_SERVER:-cortexlabs/metrics-server:$CORTEX_VERSION_STABLE}"
-
-export CORTEX_ENABLE_TELEMETRY="${CORTEX_ENABLE_TELEMETRY:-""}"
-
-##########################
-### TOP-LEVEL COMMANDS ###
-##########################
-
-function install_eks() {
-  echo
-  docker run -it --entrypoint /root/install_eks.sh \
-    -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-    -e CORTEX_CLUSTER=$CORTEX_CLUSTER \
-    -e CORTEX_REGION=$CORTEX_REGION \
-    -e CORTEX_NODE_TYPE=$CORTEX_NODE_TYPE \
-    -e CORTEX_NODES_MIN=$CORTEX_NODES_MIN \
-    -e CORTEX_NODES_MAX=$CORTEX_NODES_MAX \
-    $CORTEX_IMAGE_MANAGER
-}
-
-function uninstall_eks() {
-  echo
-  docker run -it --entrypoint /root/uninstall_eks.sh \
-    -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-    -e CORTEX_CLUSTER=$CORTEX_CLUSTER \
-    -e CORTEX_REGION=$CORTEX_REGION \
-    $CORTEX_IMAGE_MANAGER
-}
-
-function install_cortex() {
-  echo
-  docker run -it --entrypoint /root/install_cortex.sh \
-    -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-    -e CORTEX_CLUSTER=$CORTEX_CLUSTER \
-    -e CORTEX_REGION=$CORTEX_REGION \
-    -e CORTEX_NAMESPACE=$CORTEX_NAMESPACE \
-    -e CORTEX_NODE_TYPE=$CORTEX_NODE_TYPE \
-    -e CORTEX_LOG_GROUP=$CORTEX_LOG_GROUP \
-    -e CORTEX_BUCKET=$CORTEX_BUCKET \
-    -e CORTEX_IMAGE_FLUENTD=$CORTEX_IMAGE_FLUENTD \
-    -e CORTEX_IMAGE_NGINX_BACKEND=$CORTEX_IMAGE_NGINX_BACKEND \
-    -e CORTEX_IMAGE_NGINX_CONTROLLER=$CORTEX_IMAGE_NGINX_CONTROLLER \
-    -e CORTEX_IMAGE_OPERATOR=$CORTEX_IMAGE_OPERATOR \
-    -e CORTEX_IMAGE_SPARK=$CORTEX_IMAGE_SPARK \
-    -e CORTEX_IMAGE_SPARK_OPERATOR=$CORTEX_IMAGE_SPARK_OPERATOR \
-    -e CORTEX_IMAGE_TF_SERVE=$CORTEX_IMAGE_TF_SERVE \
-    -e CORTEX_IMAGE_TF_TRAIN=$CORTEX_IMAGE_TF_TRAIN \
-    -e CORTEX_IMAGE_TF_API=$CORTEX_IMAGE_TF_API \
-    -e CORTEX_IMAGE_PYTHON_PACKAGER=$CORTEX_IMAGE_PYTHON_PACKAGER \
-    -e CORTEX_IMAGE_TF_SERVE_GPU=$CORTEX_IMAGE_TF_SERVE_GPU \
-    -e CORTEX_IMAGE_TF_TRAIN_GPU=$CORTEX_IMAGE_TF_TRAIN_GPU \
-    -e CORTEX_IMAGE_ONNX_SERVE=$CORTEX_IMAGE_ONNX_SERVE \
-    -e CORTEX_IMAGE_ONNX_SERVE_GPU=$CORTEX_IMAGE_ONNX_SERVE_GPU \
-    -e CORTEX_IMAGE_CLUSTER_AUTOSCALER=$CORTEX_IMAGE_CLUSTER_AUTOSCALER \
-    -e CORTEX_IMAGE_NVIDIA=$CORTEX_IMAGE_NVIDIA \
-    -e CORTEX_IMAGE_METRICS_SERVER=$CORTEX_IMAGE_METRICS_SERVER \
-    -e CORTEX_ENABLE_TELEMETRY=$CORTEX_ENABLE_TELEMETRY \
-    $CORTEX_IMAGE_MANAGER
-}
-
-function uninstall_operator() {
-  echo
-  docker run -it --entrypoint /root/uninstall_operator.sh \
-    -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-    -e CORTEX_CLUSTER=$CORTEX_CLUSTER \
-    -e CORTEX_REGION=$CORTEX_REGION \
-    -e CORTEX_NAMESPACE=$CORTEX_NAMESPACE \
-    $CORTEX_IMAGE_MANAGER
-}
-
-function info() {
-  echo
-  docker run -it --entrypoint /root/info.sh \
-    -e AWS_ACCESS_KEY_ID=$AWS_ACCESS_KEY_ID \
-    -e AWS_SECRET_ACCESS_KEY=$AWS_SECRET_ACCESS_KEY \
-    -e CORTEX_CLUSTER=$CORTEX_CLUSTER \
-    -e CORTEX_REGION=$CORTEX_REGION \
-    -e CORTEX_NAMESPACE=$CORTEX_NAMESPACE \
-    $CORTEX_IMAGE_MANAGER
-}
-
-################
-### CHECK OS ###
-################
-
-case "$OSTYPE" in
-  darwin*)  PARSED_OS="darwin" ;;
-  linux*)   PARSED_OS="linux" ;;
-  *)        echo -e "\nerror: only mac and linux are supported"; exit 1 ;;
-esac
-
-#############################
-### DEPENDENCY MANAGEMENT ###
-#############################
-
-function check_dep_curl() {
-  if ! command -v curl >/dev/null; then
-    echo -e "\nerror: please install \`curl\`"
-    exit 1
-  fi
-}
-
-function install_cli() {
-  set -e
-
-  check_dep_curl
-
-  echo -e "\nInstalling the Cortex CLI (/usr/local/bin/cortex) ..."
-
-  CORTEX_SH_TMP_DIR="$HOME/.cortex-sh-tmp"
-  rm -rf $CORTEX_SH_TMP_DIR && mkdir -p $CORTEX_SH_TMP_DIR
-  curl -s -o $CORTEX_SH_TMP_DIR/cortex https://s3-us-west-2.amazonaws.com/get-cortex/$CORTEX_VERSION_STABLE/cli/$PARSED_OS/cortex
-  chmod +x $CORTEX_SH_TMP_DIR/cortex
-
-  if [ $(id -u) = 0 ]; then
-    mv -f $CORTEX_SH_TMP_DIR/cortex /usr/local/bin/cortex
-  else
-    ask_sudo
-    sudo mv -f $CORTEX_SH_TMP_DIR/cortex /usr/local/bin/cortex
-  fi
-
-  rm -rf $CORTEX_SH_TMP_DIR
-  echo "✓ Installed the Cortex CLI"
-
-  bash_profile_path=$(get_bash_profile)
-  if [ ! "$bash_profile_path" = "" ]; then
-    if ! grep -Fxq "source <(cortex completion)" "$bash_profile_path"; then
-      echo
-      read -p "Would you like to modify your bash profile ($bash_profile_path) to enable cortex command completion and the cx alias? [Y/n] " -n 1 -r
-      echo
-      if [[ $REPLY =~ ^[Yy]$ ]]; then
-        echo -e "\nsource <(cortex completion)" >> $bash_profile_path
-        echo "✓ Your bash profile ($bash_profile_path) has been updated"
-        echo
-        echo "Note: \`bash_completion\` must be installed on your system for cortex command completion to function properly"
-        echo
-        echo "Command to update your current terminal session:"
-        echo "  source $bash_profile_path"
-      else
-        echo "Your bash profile has not been modified. If you would like to modify it manually, add this line to your bash profile:"
-        echo "  source <(cortex completion)"
-        echo "Note: \`bash_completion\` must be installed on your system for cortex command completion to function properly"
-      fi
-    fi
-  else
-    echo -e "\nIf your would like to enable cortex command completion and the cx alias, add this line to your bash profile:"
-    echo "  source <(cortex completion)"
-    echo "Note: \`bash_completion\` must be installed on your system for cortex command completion to function properly"
-  fi
-}
-
-function uninstall_cli() {
-  set -e
-
-  rm -rf $HOME/.cortex
-
-  if ! command -v cortex >/dev/null; then
-    echo -e "\nThe Cortex CLI is not installed"
-    return
-  fi
-
-  if [[ ! -f /usr/local/bin/cortex ]]; then
-    echo -e "\nThe Cortex CLI was not found at /usr/local/bin/cortex, please uninstall it manually"
-    return
-  fi
-
-  if [ $(id -u) = 0 ]; then
-    rm /usr/local/bin/cortex
-  else
-    ask_sudo
-    sudo rm /usr/local/bin/cortex
-  fi
-  echo -e "\n✓ Uninstalled the Cortex CLI"
-
-  bash_profile_path=$(get_bash_profile)
-  if [ ! "$bash_profile_path" = "" ]; then
-    if grep -Fxq "source <(cortex completion)" "$bash_profile_path"; then
-      echo
-      read -p "Would you like to remove \"source <(cortex completion)\" from your bash profile ($bash_profile_path)? [Y/n] " -n 1 -r
-      echo
-      if [[ $REPLY =~ ^[Yy]$ ]]; then
-        sed '/^source <(cortex completion)$/d' "$bash_profile_path" > "${bash_profile_path}_cortex_modified" && mv -f "${bash_profile_path}_cortex_modified" "$bash_profile_path"
-        echo "✓ Your bash profile ($bash_profile_path) has been updated"
-      fi
-    fi
-  fi
-}
-
-function get_bash_profile() {
-  if [ "$PARSED_OS" = "darwin" ]; then
-    if [ -f $HOME/.bash_profile ]; then
-      echo $HOME/.bash_profile
-      return
-    elif [ -f $HOME/.bashrc ]; then
-      echo $HOME/.bashrc
-      return
-    fi
-  else
-    if [ -f $HOME/.bashrc ]; then
-      echo $HOME/.bashrc
-      return
-    elif [ -f $HOME/.bash_profile ]; then
-      echo $HOME/.bash_profile
-      return
-    fi
-  fi
-
-  echo ""
-}
-
-function ask_sudo() {
-  if ! sudo -n true 2>/dev/null; then
-    echo -e "\nPlease enter your sudo password"
-  fi
-}
-
-function prompt_for_telemetry() {
-  if [ "$CORTEX_ENABLE_TELEMETRY" != "true" ] && [ "$CORTEX_ENABLE_TELEMETRY" != "false" ]; then
-    while true
-    do
-      echo
-      read -p "Would you like to help improve Cortex by anonymously sending error reports and usage stats to the dev team? [Y/n] " -n 1 -r
-      echo
-      if [[ $REPLY =~ ^[Yy]$ ]]; then
-        export CORTEX_ENABLE_TELEMETRY=true
-        break
-      elif [[ $REPLY =~ ^[Nn]$ ]]; then
-        export CORTEX_ENABLE_TELEMETRY=false
-        break
-      fi
-      echo "Unexpected value, please enter \"Y\" or \"n\""
-    done
-  fi
-}
-
-############
-### HELP ###
-############
-
-function show_help() {
-  echo "
-Usage:
-  ./cortex.sh command [sub-command] [flags]
-
-Available Commands:
-  install             install Cortex
-  uninstall           uninstall Cortex
-  update              update Cortex
-  info                information about Cortex
-
-  install cli         install the Cortex CLI
-  uninstall cli       uninstall the Cortex CLI
-
-Flags:
-  -c, --config  path to a Cortex config file
-  -h, --help
-"
-}
-
-######################
-### ARG PROCESSING ###
-######################
-
-arg1=${1:-""}
-arg2=${2:-""}
-arg3=${3:-""}
-
-if [ -z "$arg1" ]; then
-  show_help
-  exit 0
-fi
-
-if [ "$arg1" = "install" ]; then
-  if [ ! "$arg3" = "" ]; then
-    echo -e "\nerror: too many arguments for install command"
-    show_help
-    exit 1
-  elif [ "$arg2" = "" ]; then
-    prompt_for_telemetry && install_eks && install_cortex && info
-  elif [ "$arg2" = "cli" ]; then
-    install_cli
-  elif [ "$arg2" = "" ]; then
-    echo -e "\nerror: missing subcommand for install"
-    show_help
-    exit 1
-  else
-    echo -e "\nerror: invalid subcommand for install: $arg2"
-    show_help
-    exit 1
-  fi
-elif [ "$arg1" = "uninstall" ]; then
-  if [ ! "$arg3" = "" ]; then
-    echo -e "\nerror: too many arguments for uninstall command"
-    show_help
-    exit 1
-  elif [ "$arg2" = "" ]; then
-    uninstall_eks
-  elif [ "$arg2" = "cli" ]; then
-    uninstall_cli
-  elif [ "$arg2" = "" ]; then
-    echo -e "\nerror: missing subcommand for uninstall"
-    show_help
-    exit 1
-  else
-    echo -e "\nerror: invalid subcommand for uninstall: $arg2"
-    show_help
-    exit 1
-  fi
-elif [ "$arg1" = "update" ]; then
-  if [ ! "$arg2" = "" ]; then
-    echo -e "\nerror: too many arguments for get command"
-    show_help
-    exit 1
-  else
-    uninstall_operator && install_cortex
-  fi
-elif [ "$arg1" = "info" ]; then
-  if [ ! "$arg2" = "" ]; then
-    echo -e "\nerror: too many arguments for get command"
-    show_help
-    exit 1
-  else
-    info
-  fi
-else
-  echo -e "\nerror: unknown command: $arg1"
-  show_help
-  exit 1
-fi

cortex.yaml

@@ -1,11 +0,0 @@
-# cortex.yaml
-- kind: deployment
-  name: text
-- kind: api
-  name: generator
-  model: s3://cortex-examples/gpt-2/774M/1539234541.zip
-  request_handler: encoder.py
-  compute:
-    gpu: 1
-    min_replicas: 1
-    max_replicas: 1

generator/tf/generator_local.py

@@ -3,16 +3,16 @@ import os
 import numpy as np
 import tensorflow as tf
 
-import gpt2.src.model as model
+from src.model import *
 from tensorflow.contrib import predictor
-import gpt2.src.sample as sample
-import gpt2.src.encoder as encoder
-from utils import *
+from src.sample import *
+from src.encoder import *
+import pdb
 
 pos_action_starts = ["You attack", "You tell", "You use", "You go"]
 
 
-class StoryGenerator():
+class LocalGenerator():
 
     def __init__(self, sess, length=75, temperature=0.9, top_k=40):
     
@@ -24,7 +24,9 @@ class StoryGenerator():
         self.enc = encoder.get_encoder(model_path)
         hparams = model.default_hparams()
         with open(os.path.join(model_path, 'hparams.json')) as f:
-            hparams.override_from_dict(json.load(f))  
+            hparams.override_from_dict(json.load(f))
+
+        pdb.set_trace()
 
         self.context = tf.placeholder(tf.int32, [batch_size, None])
         np.random.seed(seed)
@@ -38,8 +40,7 @@ class StoryGenerator():
         saver = tf.train.Saver()
         ckpt = tf.train.latest_checkpoint(model_path)
         saver.restore(self.sess, ckpt)
-            
-        
+
     def generate(self, prompt):
         context_tokens = self.enc.encode(prompt)
         out = self.sess.run(self.output, feed_dict={
@@ -48,53 +49,26 @@ class StoryGenerator():
 
         text = self.enc.decode(out[0])
         return text
-        
-    def generate_story_block(self, prompt):
-        block = self.generate(prompt)
-        block = cut_trailing_sentence(block)
-        block = story_replace(block)
-        
-        return block
-        
-    def generate_action_options(self, prompt, action_starts=pos_action_starts):
-    
-        possible_actions = []
-        for phrase in action_starts:
-            action = phrase + self.generate(prompt + phrase)
-            action = first_sentence(action)
-            possible_actions.append(action)
-            
-        return possible_actions
-        
-    def generate_action_result(self, prompt, phrase):
-        action = phrase + self.generate(prompt + phrase)
-        action_result = cut_trailing_sentence(action)
-        action_result = story_replace(action_result)
-        
-        action = first_sentence(action)
-        
-
-        return action, action_result
-
 
 def save_model():
     length=75
     temperature=0.9
     top_k=40
+    os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
     
     with tf.Session() as sess:
         seed = None
         batch_size=None
-        model_path='gpt2/models/117M'
+        model_path='models/774M'
 
-        hparams = model.default_hparams()
+        hparams = default_hparams()
         with open(os.path.join(model_path, 'hparams.json')) as f:
             hparams.override_from_dict(json.load(f))  
 
         context = tf.placeholder(tf.int32, [batch_size, None])
         np.random.seed(seed)
         tf.set_random_seed(seed)
-        output = sample.sample_sequence(
+        output = sample_sequence(
             hparams=hparams, length=length,
             context=context,
             batch_size=batch_size,
@@ -105,33 +79,20 @@ def save_model():
         ckpt = tf.train.latest_checkpoint(model_path)
         saver.restore(sess, ckpt)
 
-        tf.saved_model.simple_save(sess, "./saved2", inputs={"context": context}, outputs={"output": output})
-
-
-def generate_gpu_config(memory_fraction):
-    config = tf.ConfigProto()
-    config.gpu_options.allow_growth = True
-    config.gpu_options.per_process_gpu_memory_fraction = memory_fraction
-    return config
-
-def run_interactive():
-    pass
-
+        tf.saved_model.simple_save(sess, "./saved_model", inputs={"context": context}, outputs={"output": output})
 
 def load_model():
-    # Set your memory fraction equal to a value less than 1, 0.6 is a good starting point.
-    # If no fraction is defined, the tensorflow algorithm may run into gpu out of memory problems.
     fraction = 0.6
-    config = config=generate_gpu_config(fraction)
+    config = config = generate_gpu_config(fraction)
     path_to_graph = "./saved"
 
-    #tf.saved_model.loader.load(
-     #   session,
-      #  [tf.saved_model.tag_constants.SERVING],
-       # path_to_graph)
+    # tf.saved_model.loader.load(
+    #   session,
+    #  [tf.saved_model.tag_constants.SERVING],
+    # path_to_graph)
 
-    #output = session.graph.get_tensor_by_name('output:0')
-    #context = session.graph.get_tensor_by_name('context:0')
+    # output = session.graph.get_tensor_by_name('output:0')
+    # context = session.graph.get_tensor_by_name('context:0')
     model_path = 'gpt2/models/117M'
     enc = encoder.get_encoder(model_path)
 
@@ -143,6 +104,7 @@ def load_model():
 
     return (output, session)
 
+
 if __name__ == '__main__':
    save_model()
     

generator/tf/models/774M/.gitignore

@@ -0,0 +1,2 @@
+model.ckpt.data-00000-of-00001
+

generator/tf/models/774M/hparams.json

@@ -0,0 +1,7 @@
+{
+  "n_vocab": 50257,
+  "n_ctx": 1024,
+  "n_embd": 1280,
+  "n_head": 20,
+  "n_layer": 36
+}

generator/tf/src/sample.py

@@ -1,6 +1,6 @@
 import tensorflow as tf
 
-import gpt2.src.model as model
+from src.model import *
 
 def top_k_logits(logits, k):
     if k == 0:
@@ -30,11 +30,11 @@ def sample_sequence(*, hparams, length, start_token=None, batch_size=None, conte
         context = tf.fill([batch_size, 1], start_token)
 
     def step(hparams, tokens, past=None):
-        lm_output = model.model(hparams=hparams, X=tokens, past=past, reuse=tf.AUTO_REUSE)
+        lm_output = model(hparams=hparams, X=tokens, past=past, reuse=tf.AUTO_REUSE)
 
         logits = lm_output['logits'][:, :, :hparams.n_vocab]
         presents = lm_output['present']
-        presents.set_shape(model.past_shape(hparams=hparams, batch_size=batch_size))
+        presents.set_shape(past_shape(hparams=hparams, batch_size=batch_size))
         return {
             'logits': logits,
             'presents': presents,
@@ -69,7 +69,7 @@ def sample_sequence(*, hparams, length, start_token=None, batch_size=None, conte
                 context,
             ],
             shape_invariants=[
-                tf.TensorShape(model.past_shape(hparams=hparams, batch_size=batch_size)),
+                tf.TensorShape(past_shape(hparams=hparams, batch_size=batch_size)),
                 tf.TensorShape([batch_size]),
                 tf.TensorShape([batch_size, None]),
             ],

generator/web/web_generator.py

@@ -0,0 +1,44 @@
+from generator.tf.src.encoder import *
+import googleapiclient.discovery
+
+pos_action_starts = ["You attack", "You tell", "You use", "You go"]
+project = "ai-adventure"
+model = "generator_v1"
+version = "version2"
+
+class WebGenerator():
+
+    def __init__(self, credentials_file):
+        os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = credentials_file
+        model_path = './generator/tf/models/117M'
+        self.enc = get_encoder(model_path)
+
+    def predict(self, context_tokens):
+        service = googleapiclient.discovery.build('ml', 'v1')
+        name = 'projects/{}/models/{}'.format(project, model)
+        instance = context_tokens
+
+        if version is not None:
+            name += '/versions/{}'.format(version)
+
+        response = service.projects().predict(
+            name=name,
+            body={'instances': [{'context': instance}]}
+        ).execute()
+
+        if 'error' in response:
+            raise RuntimeError(response['error'])
+
+        return response['predictions']
+
+    def generate(self, prompt):
+        while (True):
+            context_tokens = self.enc.encode(prompt)
+            try:
+                pred = self.predict(context_tokens)
+                pred = pred[0]["output"][len(context_tokens):]
+                output = self.enc.decode(pred)
+                return output
+            except:
+                print("generate request failed, trying again")
+                continue

gpt2/models/117M/.gitignore

@@ -1 +0,0 @@
-model*

gpt2/models/117M/hparams.json

@@ -1,7 +0,0 @@
-{
-  "n_vocab": 50257,
-  "n_ctx": 1024,
-  "n_embd": 768,
-  "n_head": 12,
-  "n_layer": 12
-}

main.py

@@ -1,133 +1,17 @@
-# Copyright 2018 Google LLC
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# [START gae_python37_render_template]
-import datetime
 from flask import g
+from flask import session
 import os
-import googleapiclient.discovery
-from utils import *
-from google.cloud import storage
-from google import cloud
+from story.utils import *
 import json
 from flask import Flask, render_template, request, abort
-from flask import Response
-import requests
-import pdb
-import sys
-from generator import StoryGenerator
-import gpt2.src.encoder as encoder
+from story.story_manager import *
+from generator.web.web_generator import *
+from other.caching import *
 
-
-# App Info
-phrases = [" You attack", " You use", " You tell", " You go"]
-prompts = ["You enter a dungeon with your trusty sword and shield. You are searching for the evil necromancer who killed your family. You've heard that he resides at the bottom of the dungeon, guarded by legions of the undead. You enter the first door and see"]
-continuing_prompts = ["You are in a dungeon with your sword and shield. You are on a quest to defeat the necromancer. This dungeon is full of zombie and skeletons."]
 app = Flask(__name__)
-
-# Encoder Info
-encoder_path='gpt2/models/117M'
-enc = encoder.get_encoder(encoder_path)
-
-# Model/Cache Info
-project = "ai-adventure"
-model = "generator_v1"
-version = "version2"
-os.environ['GOOGLE_APPLICATION_CREDENTIALS']="./AI-Adventure-2bb65e3a4e2f.json"
-storage_client = storage.Client()
-bucket = storage_client.get_bucket("dungeon-cache")
-
-# Local generator functionality
-RUN_LOCAL = True
-session = None
-local_generator = None
-def get_local_generator():
-    if "gen" not in g:
-        if "sess" not in g:
-            g.sess = tf.Session()
-        g.gen = StoryGenerator(g.sess)
-
-    return g.gen
+app.secret_key = '#d\xe0\xd1\xfb\xee\xa4\xbb\xd0\xf0/e)\xb5g\xdd<`\xc7\xa5\xb0-\xb8d0S'
 
 
-@app.teardown_appcontext
-def teardown_sess(_):
-    sess = g.pop("sess", None)
-
-    if sess is not None:
-        sess.close()
-
-def predict(context_tokens):
-    service = googleapiclient.discovery.build('ml', 'v1')
-    name = 'projects/{}/models/{}'.format(project, model)
-    instance = context_tokens
-
-    if version is not None:
-        name += '/versions/{}'.format(version)
-
-    response = service.projects(). predict(
-        name=name,
-        body={'instances': [{'context': instance}]}
-    ).execute()
-
-    if 'error' in response:
-        raise RuntimeError(response['error'])
-
-    return response['predictions']
-    
-    
-def generate(prompt):
-
-    while(True):
-        context_tokens = enc.encode(prompt)
-        try:
-            pred = predict(context_tokens)
-            pred = pred[0]["output"][len(context_tokens):]
-            output = enc.decode(pred)
-            return output
-        except:
-            print("generate request failed, trying again")
-            continue
-
-
-def generate_story_block(prompt, local=False):
-
-    if local:
-        generator = get_local_generator()
-        block = generator.generate(prompt)
-    else:
-        block = generate(prompt)
-
-    block = cut_trailing_sentence(block)
-    block = story_replace(block)
-    return block
-
-
-def generate_action_result(prompt, phrase, local=False):
-
-    if local:
-        generator = get_local_generator()
-        action = phrase + generator.generate(prompt + phrase)
-    else:
-        action = phrase + generate(prompt + phrase)
-
-    action_result = cut_trailing_sentence(action)
-    action_result = story_replace(action_result)
-    action = first_sentence(action)
-
-    return action, action_result
-    
 @app.route('/')
 def root():
     seed = -1
@@ -142,49 +26,21 @@ def rootseed(seed):
     else:
         seed = int(seed)
     data = {'seed': seed}
+    session["seed"] = seed
     return render_template('index.html', data=data)
 
+
 @app.route('/index.html')
 def index():
     data = {'seed': -1}
     return render_template('index.html', data=data)
 
+
 @app.route('/about.html')
 def about():
     return render_template('about.html')
 
 
-def cache_file(seed, prompt_num, choices, response, tag):
-    return
-    # blob_file_name = "prompt" + str(prompt_num) + "/seed" + str(seed) + "/" + tag
-    # for action in choices:
-    #     blob_file_name = blob_file_name + str(action)
-    # blob = bucket.blob(blob_file_name)
-    #
-    # blob.upload_from_string(response)
-    #
-    # print("File ", blob_file_name, " cached")
-
-
-def retrieve_from_cache(seed, prompt_num, choices, tag):
-    return None
-    # blob_file_name = "prompt" + str(prompt_num) + "/seed" + str(seed) + "/" + tag
-    #
-    # for action in choices:
-    #     blob_file_name = blob_file_name + str(action)
-    #
-    # blob = bucket.blob(blob_file_name)
-    #
-    # if blob.exists(storage_client):
-    #     result = blob.download_as_string().decode("utf-8")
-    #     print(blob_file_name, " found in cache")
-    # else:
-    #     result = None
-    #     print(blob_file_name, " not found in cache")
-    #
-    # return result
-
-
 @app.route('/generate', methods=['POST'])
 def story_request():
     print("****Generating Story****")
@@ -192,15 +48,17 @@ def story_request():
     prompt_num = int(request.form["prompt_num"])
     gen_actions = request.form["actions"]
 
+    print("Session Seed is ", session["seed"])
+
     if int(seed) < 0 or int(seed) > 100:
-        print("Invalid seed: " + seed)
+        #print("Invalid seed: " + seed)
         abort(404)
 
     if gen_actions == "true":
 
         #prompt = request.form["prompt"]
         choices = json.loads(request.form["choices"])
-        print("Getting response for seed ", seed, " prompt_num ", prompt_num, " and choices ", choices)
+        #print("Getting response for seed ", seed, " prompt_num ", prompt_num, " and choices ", choices)
         
         action_results = retrieve_from_cache(seed, prompt_num, choices, "choices")
 
@@ -209,13 +67,13 @@ def story_request():
         else:
             last_action_result = request.form["last_action_result"]
             prompt = continuing_prompts[prompt_num] + last_action_result
-            print("\n\nAction prompt is \n ", prompt)
+            #print("\n\nAction prompt is \n ", prompt)
             action_results = [generate_action_result(prompt, phrase, local=RUN_LOCAL) for phrase in phrases]
             response = json.dumps(action_results)
             cache_file(seed, prompt_num, choices, response, "choices")
     else:
 
-        print("Getting response for seed ", seed, " prompt_num ", prompt_num)
+        #print("Getting response for seed ", seed, " prompt_num ", prompt_num)
         result = retrieve_from_cache(seed, prompt_num, [], "story")
 
         if result is not None:
@@ -225,68 +83,10 @@ def story_request():
             response = generate_story_block(prompt, local=RUN_LOCAL)
             cache_file(seed, prompt_num, [], response, "story")
 
-    print("\nGenerated response is: \n", response)
-    print("")
+    #print("\nGenerated response is: \n", response)
+    #print("")
 
     return response
-    
-    
-def generate_cache():   
-
-    start_seed = int(sys.argv[1])
-    end_seed = int(sys.argv[2])
-
-    # Generate story sections
-    prompt_num = 0
-    action_queue = []
-    prompt = prompts[prompt_num]
-    for seed in range(start_seed,end_seed):
-        result = retrieve_from_cache(seed, prompt_num, [], "story")
-        if result is not None:
-            response = result
-        else:
-            prompt = prompts[prompt_num]
-            #print("\n Story prompt is ", prompt)
-            response = generate_story_block(prompt)
-            #print("\n Story response is ", response)
-            cache_file(seed, prompt_num, [], response, "story")
-            
-        action_queue.append([seed,0,[],response])
-    
-    while(True):
-        
-        next_gen = action_queue.pop(0)
-        seed = next_gen[0]
-        prompt_num = next_gen[1]
-        choices = next_gen[2]
-        last_action_result = next_gen[3]
-        
-        action_results = retrieve_from_cache(seed, prompt_num, choices, "choices")
-        
-        if action_results is not None:
-            response = action_results
-            
-        else:
-            if len(choices) is 0:
-                prompt = prompts[prompt_num] + last_action_result
-            else:
-                prompt = continuing_prompts[prompt_num] + last_action_result
-            #print("\n\n Action prompt is \n ", prompt)
-            action_results = [generate_action_result(prompt, phrase) for phrase in phrases]
-            response = json.dumps(action_results)
-            
-            #print("\n\n Action 
-            cache_file(seed, prompt_num, choices, response, "choices")
-            
-        un_jsoned = json.loads(response)
-        for j in range(4):
-            new_choices = choices[:]
-            new_choices.append(j)
-            action_queue.append([seed, 0, new_choices,  un_jsoned[j][1]])
 
 if __name__ == '__main__':
-    app.run(host='0.0.0.0', port=8080)
-
-
-
-# [START gae_python37_render_template]
+    app.run(host='0.0.0.0', port=8080)

other/caching.py

@@ -0,0 +1,44 @@
+from flask import g
+from flask import session
+import os
+import googleapiclient.discovery
+from story.utils import *
+from google.cloud import storage
+import json
+from flask import Flask, render_template, request, abort
+from generator import StoryGenerator
+import gpt2.src.encoder as encoder
+
+# Model/Cache Info
+storage_client = storage.Client()
+bucket = storage_client.get_bucket("dungeon-cache")
+
+
+def cache_file(seed, prompt_num, choices, response, tag):
+
+    blob_file_name = "prompt" + str(prompt_num) + "/seed" + str(seed) + "/" + tag
+    for action in choices:
+        blob_file_name = blob_file_name + str(action)
+    blob = bucket.blob(blob_file_name)
+
+    blob.upload_from_string(response)
+
+    print("File ", blob_file_name, " cached")
+
+
+def retrieve_from_cache(seed, prompt_num, choices, tag):
+    blob_file_name = "prompt" + str(prompt_num) + "/seed" + str(seed) + "/" + tag
+
+    for action in choices:
+        blob_file_name = blob_file_name + str(action)
+
+    blob = bucket.blob(blob_file_name)
+
+    if blob.exists(storage_client):
+        result = blob.download_as_string().decode("utf-8")
+        print(blob_file_name, " found in cache")
+    else:
+        result = None
+        print(blob_file_name, " not found in cache")
+
+    return result

other/generate_cache.py

@@ -0,0 +1,51 @@
+def generate_cache():
+    start_seed = int(sys.argv[1])
+    end_seed = int(sys.argv[2])
+
+    # Generate story sections
+    prompt_num = 0
+    action_queue = []
+    prompt = prompts[prompt_num]
+    for seed in range(start_seed, end_seed):
+        result = retrieve_from_cache(seed, prompt_num, [], "story")
+        if result is not None:
+            response = result
+        else:
+            prompt = prompts[prompt_num]
+            # print("\n Story prompt is ", prompt)
+            response = generate_story_block(prompt)
+            # print("\n Story response is ", response)
+            cache_file(seed, prompt_num, [], response, "story")
+
+        action_queue.append([seed, 0, [], response])
+
+    while (True):
+
+        next_gen = action_queue.pop(0)
+        seed = next_gen[0]
+        prompt_num = next_gen[1]
+        choices = next_gen[2]
+        last_action_result = next_gen[3]
+
+        action_results = retrieve_from_cache(seed, prompt_num, choices, "choices")
+
+        if action_results is not None:
+            response = action_results
+
+        else:
+            if len(choices) is 0:
+                prompt = prompts[prompt_num] + last_action_result
+            else:
+                prompt = continuing_prompts[prompt_num] + last_action_result
+            # print("\n\n Action prompt is \n ", prompt)
+            action_results = [generate_action_result(prompt, phrase) for phrase in phrases]
+            response = json.dumps(action_results)
+
+            # print("\n\n Action
+            cache_file(seed, prompt_num, choices, response, "choices")
+
+        un_jsoned = json.loads(response)
+        for j in range(4):
+            new_choices = choices[:]
+            new_choices.append(j)
+            action_queue.append([seed, 0, new_choices, un_jsoned[j][1]])

story/story_manager.py

@@ -0,0 +1,112 @@
+from story.utils import *
+
+
+class Story():
+
+    def __init__(self, story_start):
+
+        self.story_start = story_start
+
+        # list of actions. First action is the prompt length should always equal that of story blocks
+        self.actions = []
+
+        # list of story blocks first story block follows prompt and is intro story
+        self.results = []
+
+    def add_to_story(self, action, story_block):
+        self.actions.append(action)
+        self.results.append(story_block)
+
+    def latest_result(self):
+        if len(self.results) > 0:
+            return self.results[-1]
+        else:
+            return ""
+
+    def __str__(self):
+        story_list = [self.story_start]
+        for i in range(len(self.results)):
+            story_list.append(self.actions[i])
+            story_list.append(self.results[i])
+
+        return "".join(story_list)
+
+
+class UnconstrainedStoryManager():
+
+    def __init__(self, generator, story_prompt):
+        self.generator = generator
+
+        block = self.generator.generate(story_prompt)
+        block = cut_trailing_sentence(block)
+        block = story_replace(block)
+        story_start = story_prompt + block
+
+        self.story = Story(story_start)
+
+    def act(self, action_choice):
+
+        result = self.generate_result(action_choice)
+        self.story.add_to_story(action_choice, result)
+        return result
+
+    def story_context(self):
+        return self.story.latest_result()
+
+    def generate_result(self, action):
+        block = self.generator.generate(self.story_context() + action)
+        block = cut_trailing_sentence(block)
+        block = story_replace(block)
+        return block
+
+
+class ConstrainedStoryManager():
+
+    def __init__(self, generator, story_prompt):
+        self.generator = generator
+        self.action_phrases = ["You attack", "You tell", "You use", "You go"]
+        block = self.generator.generate(story_prompt)
+        block = cut_trailing_sentence(block)
+        block = story_replace(block)
+        story_start = story_prompt + block
+        self.story = Story(story_start)
+        self.possible_action_results = None
+
+    def get_possible_actions(self):
+        if self.possible_action_results is None:
+            self.possible_action_results = self.get_action_results()
+
+        return [action_result[0] for action_result in self.possible_action_results]
+
+    def act(self, action_choice_str):
+
+        try:
+            action_choice = int(action_choice_str)
+        except:
+            print("Error invalid choice.")
+            return None, None
+
+        if action_choice < 0 or action_choice >= len(self.action_phrases):
+            print("Error invalid choice.")
+            return None, None
+
+        action, result = self.possible_action_results[action_choice]
+        self.story.add_to_story(action, result)
+        self.possible_action_results = self.get_action_results()
+        return result, self.get_possible_actions()
+
+    def story_context(self):
+        return self.story.latest_result()
+
+    def get_action_results(self):
+        return [self.generate_action_result(self.story_context(), phrase) for phrase in self.action_phrases]
+
+    def generate_action_result(self, prompt, phrase):
+        action = phrase + self.generator.generate(prompt + phrase)
+        action_result = cut_trailing_sentence(action)
+
+        action, result = split_first_sentence(action_result)
+        result = story_replace(action_result)
+        action = action_replace(action)
+
+        return action, result

story/utils.py

@@ -52,18 +52,19 @@ def text_replace(text):
     return text
     
     
-def first_sentence(text):
+def split_first_sentence(text):
     first_period = text.find('.')
     first_exclamation = text.find('!')
     
     if first_exclamation < first_period and first_exclamation > 0:
-        text = text[0:first_exclamation+1]
+        split_point = first_exclamation+1
     elif first_period > 0:
-        text = text[0:first_period+1]
+        split_point = first_period+1
     else:
-        return text[0:20]
+        split_point = text[0:20]
         
-    return text
+    return text[0:split_point], text[split_point:]
+
     
 def all_but_first(text):
     first_period = text.find('.')

templates/index.html

@@ -13,7 +13,8 @@
     ga('send', 'pageview');
     </script>
     <!-- End Google Analytics -->
-    <title>Dungeon</title> 
+    <link rel="shortcut icon" href="{{ url_for('static', filename='favicon.ico') }}">
+    <title>AI Dungeon</title>
     <script type="text/javascript" src="https://ajax.googleapis.com/ajax/libs/jquery/1.4.2/jquery.min.js"></script> 
     <script type="text/javascript">
         var seed = {{ data.seed }}