finalize

app.py

+from ast import Return
 import json
 from chatterbot import ChatBot
 from flask import Flask, jsonify, request
@@ -9,24 +10,53 @@ print('Loading Sentiment Analysis model')
 sa = Sentiment()
 
 app = Flask(__name__)
-# bot = ChatBot('Chatterbot', storage_adapter="chatterbot.storage.SQLStorageAdapter")
-# trainer = ChatterBotCorpusTrainer(bot)
-# trainer.train('./chatterbot_training_dataset/ai.yml', './chatterbot_training_dataset/emotion.yml', './chatterbot_training_dataset/conversational.yml')
-#trainer.train('./chatterbot_training_dataset/conversational.yml')
-#trainer.train('./chatterbot_training_dataset/emotion.yml')
+
+sentiment_dict = dict()
+
+score_report = list()
+
 
 @app.route('/test', methods=['POST'])
 def index_test():
+
+    global sentiment_dict
+    global score_report
+    overall_output=dict()
+
     js_text = request.get_json()
+
+    #Initialize the dictionary
+    if "start" in js_text:
+        if js_text["start"]:
+            sentiment_dict=dict()
+            score_report=list() 
+    else:
+        return jsonify({"error":"You have to define the start in the payload"})
+    
     pred=sa.get_sentiment_prediction(js_text['text'])
 
-    new_ma_val = max(pred.items(), key=operator.itemgetter(1))[0]
+    max_emotion = max(pred.items(), key=operator.itemgetter(1))[0]
+    max_value = max(pred.items(), key=operator.itemgetter(1))[1]
+    output={"text":js_text['text'], "matchedEmotion":max_emotion,"maximumValue":max_value, "emotionWithAverage":pred}
+    score_report.append(output)
+
+    if max_emotion in sentiment_dict:
+        sentiment_dict[max_emotion]+=max_value
+    else:
+        sentiment_dict[max_emotion]=max_value
+    
+    sorted_values = {k:v for k,v in sorted(sentiment_dict.items(), key=lambda x: x[1], reverse=True)}
+    overall_output={"overalEmotion":list(sorted_values.keys())[0],"overalScore":list(sorted_values.values())[0], "otherScores":dict(zip(list(sorted_values.keys())[1:],list(sorted_values.values())[1:]))}
+
+    overal_output={"overall":overall_output, "individual": score_report}
+    overal_res=json.dumps(overal_output)
 
-    #arr = [ {'emotion' : i, 'value' : pred[i]} for i in pred]
-    avg = json.dumps(pred)
+    ##clear the dictionary
+    if "end" in js_text:
+        if js_text["end"]:
+             sentiment_dict=dict() 
+             score_report=list()
 
-    #response from chatbot
-    # res = str(bot.get_response(js_text['text']))
-    return avg
+    return overal_res
 
 app.run(debug=True, port=8080)
\ No newline at end of file

sentiment_analysis.py

@@ -148,7 +148,7 @@ class Sentiment:
 
 
     def load_model_weights(self,model):
-        model.load_weights('./model_weights/sentiment_weights.h5')
+        model.load_weights('./model_weights/sentiment_weights3.h5')
         return model