smart assistant api endpoint added

backend/smart_assistant/.idea/.gitignore

+# Default ignored files
+/shelf/
+/workspace.xml

backend/smart_assistant/.idea/RL.iml

+<?xml version="1.0" encoding="UTF-8"?>
+<module type="PYTHON_MODULE" version="4">
+  <component name="NewModuleRootManager">
+    <content url="file://$MODULE_DIR$" />
+    <orderEntry type="jdk" jdkName="Python 3.8 (gym_rl)" jdkType="Python SDK" />
+    <orderEntry type="sourceFolder" forTests="false" />
+  </component>
+</module>
\ No newline at end of file

backend/smart_assistant/.idea/inspectionProfiles/Project_Default.xml

+<component name="InspectionProjectProfileManager">
+  <profile version="1.0">
+    <option name="myName" value="Project Default" />
+    <inspection_tool class="PyPackageRequirementsInspection" enabled="true" level="WARNING" enabled_by_default="true">
+      <option name="ignoredPackages">
+        <value>
+          <list size="32">
+            <item index="0" class="java.lang.String" itemvalue="opencv-python" />
+            <item index="1" class="java.lang.String" itemvalue="cmake" />
+            <item index="2" class="java.lang.String" itemvalue="numpy" />
+            <item index="3" class="java.lang.String" itemvalue="dlib" />
+            <item index="4" class="java.lang.String" itemvalue="thop" />
+            <item index="5" class="java.lang.String" itemvalue="scikit-image" />
+            <item index="6" class="java.lang.String" itemvalue="opencv-contrib-python" />
+            <item index="7" class="java.lang.String" itemvalue="wincertstore" />
+            <item index="8" class="java.lang.String" itemvalue="networkx" />
+            <item index="9" class="java.lang.String" itemvalue="tifffile" />
+            <item index="10" class="java.lang.String" itemvalue="Flask-Cors" />
+            <item index="11" class="java.lang.String" itemvalue="PyYAML" />
+            <item index="12" class="java.lang.String" itemvalue="torch" />
+            <item index="13" class="java.lang.String" itemvalue="torchvision" />
+            <item index="14" class="java.lang.String" itemvalue="mysql-connector-python" />
+            <item index="15" class="java.lang.String" itemvalue="pandas" />
+            <item index="16" class="java.lang.String" itemvalue="tqdm" />
+            <item index="17" class="java.lang.String" itemvalue="regex" />
+            <item index="18" class="java.lang.String" itemvalue="mediapipe" />
+            <item index="19" class="java.lang.String" itemvalue="seaborn" />
+            <item index="20" class="java.lang.String" itemvalue="better-profanity" />
+            <item index="21" class="java.lang.String" itemvalue="colorama" />
+            <item index="22" class="java.lang.String" itemvalue="PyWavelets" />
+            <item index="23" class="java.lang.String" itemvalue="imutils" />
+            <item index="24" class="java.lang.String" itemvalue="pytz" />
+            <item index="25" class="java.lang.String" itemvalue="torchaudio" />
+            <item index="26" class="java.lang.String" itemvalue="Flask" />
+            <item index="27" class="java.lang.String" itemvalue="Pillow" />
+            <item index="28" class="java.lang.String" itemvalue="flashtext" />
+            <item index="29" class="java.lang.String" itemvalue="unidecode" />
+            <item index="30" class="java.lang.String" itemvalue="sklearn" />
+            <item index="31" class="java.lang.String" itemvalue="nltk" />
+          </list>
+        </value>
+      </option>
+    </inspection_tool>
+  </profile>
+</component>
\ No newline at end of file

backend/smart_assistant/.idea/inspectionProfiles/profiles_settings.xml

+<component name="InspectionProjectProfileManager">
+  <settings>
+    <option name="USE_PROJECT_PROFILE" value="false" />
+    <version value="1.0" />
+  </settings>
+</component>
\ No newline at end of file

backend/smart_assistant/.idea/misc.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.8 (gym_rl)" project-jdk-type="Python SDK" />
+  <component name="PyCharmProfessionalAdvertiser">
+    <option name="shown" value="true" />
+  </component>
+</project>
\ No newline at end of file

backend/smart_assistant/.idea/modules.xml

+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/RL.iml" filepath="$PROJECT_DIR$/.idea/RL.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file

backend/smart_assistant/EmotionEnv.py

+from gym import Env
+from gym.spaces import Discrete, Box
+import numpy as np
+import random
+
+
+class EmotionEnv(Env):
+    def __init__(self):
+        # Actions we can take, down, stay, up
+        self.action_space = Discrete(3)
+        # Friends array
+        self.observation_space = Box(low=np.array([0]), high=np.array([10]))
+        # Set start emotion
+        self.state = 3 + random.randint(-3, 3)
+        # Set emotion length
+        self.emotion_length = 60
+
+    def step(self, action):
+        # should take actions by human
+        # print('This is suggested friend id', action)
+        # self.state += int(input("Enter your emotional level : "))
+
+        self.state = action
+
+        # Reduce emotion length by 1 second
+        self.emotion_length -= 1
+
+        reward = 0
+
+        # Calculate reward
+        if self.state == 0:
+            reward = -2
+        elif self.state == 1:
+            reward = -1
+        elif self.state == 2:
+            reward = 1
+        elif self.state == 3:
+            reward = 2
+        elif self.state == 4:
+            reward = 3
+        elif self.state == 5:
+            reward = 4
+
+            # Check if emotion is done
+        if self.emotion_length <= 0:
+            done = True
+        else:
+            done = False
+
+        # self.state += random.randint(-1,1)
+        # Set placeholder for info
+        info = {}
+
+        # Return step information
+        return self.state, reward, done, info
+
+    def render(self):
+        # Implement viz
+        pass
+
+    def reset(self):
+        # Reset emotion state
+        self.state = 38 + random.randint(-3, 3)
+        # Reset emotion time
+        self.shower_length = 60
+        return self.state

backend/smart_assistant/api.py

+from flask import Flask, request, url_for, redirect, render_template
+from flask_cors import CORS
+import json
+import os
+import EmotionEnv
+from pycaret.classification import *
+import numpy as np
+
+env = EmotionEnv.EmotionEnv()
+env.observation_space.sample()
+state = env.reset()
+done = False
+score = 0
+
+model = load_model('Logistic_Regression')
+
+app = Flask(__name__)
+CORS(app)
+
+
+@app.route('/', methods=['GET', 'POST'])
+def welcome():
+    return_str = '{ "status" : "app works" }'
+    return json.loads(return_str)
+
+
+@app.route('/friend_suggestion', methods=['GET', 'POST'])
+def friend_suggestion():
+    input_1 = request.form['input_1']
+    input_2 = request.form['input_2']
+    input_3 = request.form['input_3']
+
+    global score
+
+    data = np.array([['input_1', 'input_2', 'input_3'], [input_1, input_2, input_3]])
+    result = predict_model(model, data=pd.DataFrame(data=data[0:, 0:], index=data[0:, 0], columns=data[0, 0:])).iat[1, 3]
+    print(result)
+    action = env.action_space.sample()
+    n_state, reward, done, info = env.step(action)
+    score += int(result)
+
+    return_str = '{ "suggested_friend_id" : ' + str(action) + '}'
+
+    print(return_str)
+
+    return json.loads(return_str)
+
+
+if __name__ == '__main__':
+    app.run(host="0.0.0.0", port=5500, debug=True)

backend/smart_assistant/checkpoint

+model_checkpoint_path: "dqn_weights.h5f"
+all_model_checkpoint_paths: "dqn_weights.h5f"

backend/smart_assistant/readme.txt

+pip install tensorflow==2.3.0 gym keras keras-rl2 pycaret flask Flask-Cors
+pip install pycaret
+pip install numpy==1.18.0

backend/smart_assistant/test.py

+import EmotionEnv
+
+env = EmotionEnv.EmotionEnv()
+
+env.observation_space.sample()
+
+episodes = 10
+for episode in range(1, episodes + 1):
+    state = env.reset()
+    done = False
+    score = 0
+
+    while not done:
+        # env.render()
+        action = env.action_space.sample()
+        n_state, reward, done, info = env.step(action)
+        score += reward
+    print('Episode:{} Score:{}'.format(episode, score))
+

backend/smart_assistant/test_2.py

+import numpy as np
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense, Flatten
+from tensorflow.keras.optimizers import Adam
+import EmotionEnv
+from rl.agents import DQNAgent
+from rl.policy import BoltzmannQPolicy
+from rl.memory import SequentialMemory
+
+env = EmotionEnv.EmotionEnv()
+
+states = env.observation_space.shape
+actions = env.action_space.n
+
+
+def build_model(states, actions):
+    model = Sequential()
+    model.add(Dense(24, activation='relu', input_shape=states))
+    model.add(Dense(24, activation='relu'))
+    model.add(Dense(actions, activation='linear'))
+    return model
+
+
+model = build_model(states, actions)
+print(model.summary())
+
+
+def build_agent(model, actions):
+    policy = BoltzmannQPolicy()
+    memory = SequentialMemory(limit=50000, window_length=1)
+    dqn = DQNAgent(model=model, memory=memory, policy=policy,
+                   nb_actions=actions, nb_steps_warmup=10, target_model_update=1e-2)
+    return dqn
+
+
+dqn = build_agent(model, actions)
+dqn.compile(Adam(lr=1e-3), metrics=['mae'])
+dqn.fit(env, nb_steps=50000, visualize=False, verbose=1)
+
+scores = dqn.test(env, nb_episodes=100, visualize=False)
+print(np.mean(scores.history['episode_reward']))
+
+dqn.save_weights('dqn_weights.h5f', overwrite=True)

backend/smart_assistant/test_3.py

+import EmotionEnv
+
+env = EmotionEnv.EmotionEnv()
+
+env.observation_space.sample()
+
+episodes = 10
+for episode in range(1, episodes + 1):
+    state = env.reset()
+    done = False
+    score = 0
+
+    while not done:
+        # env.render()
+        action = env.action_space.sample()
+        n_state, reward, done, info = env.step(action)
+        score += reward
+    print('Episode:{} Score:{}'.format(episode, score))
\ No newline at end of file