New

EmotionDetection/__init__.py

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+#from . import emotion_detector

EmotionDetection/emotion_detection.py

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+import requests  # Import the requests library to handle HTTP requests
+import json
+
+#
+# Michael David 2025
+#
+#
+
+def emotion_detector(text_to_analyse):  # Define a function named sentiment_analyzer that takes a string input (text_to_analyse)
+     # URL of the sentiment analysis service
+    url = 'https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict'
+    # Constructing the request payload in the expected format
+    myobj = { "raw_document": { "text": text_to_analyse } }
+
+
+    # Custom header specifying the model ID for the sentiment analysis service
+    header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+
+
+    # Sending a POST request to the sentiment analysis API
+    response = requests.post(url, json=myobj, headers=header)
+    print(response)
+    if response.status_code == 400:
+        print("Invalid text! Please try again!")
+        results = {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }     
+    elif response.status_code == 200:
+
+        # Parsing the JSON response from the API
+        formatted_response = json.loads(response.text)
+
+        #convert to a set of emotions
+        emotions = formatted_response["emotionPredictions"][0]["emotion"]   
+
+        # find emotion with highest score
+        highestEmotion = max(emotions, key = emotions.get)
+
+        results = {
+            'anger': emotions["anger"],
+            'disgust': emotions["disgust"],
+            'fear': emotions["fear"],
+            'joy': emotions["joy"],
+            'sadness': emotions["sadness"],
+            'dominant_emotion': highestEmotion
+        }
+
+    return(results)
+    #return {'label': label, 'score': score}

server.py

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+"""
+This module is part of my final project
+
+"""
+from flask import Flask, render_template, request
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask("Emotion Detector")
+
+@app.route('/')
+def render_homepage():
+    """ renders home page """
+    return render_template("index.html")
+
+@app.route("/emotionDetector", methods = ["GET"])
+def sent_analyzer():
+    """ calls emotion detector function and returns response to webpage """
+    text = request.args.get('textToAnalyze')
+
+    response = emotion_detector(text)
+
+    if response["dominant_emotion"] is None:
+        result = "Invalid text! Please try again!"
+    else:
+        result = "For the given statement, the system response is"
+
+        for key, value in response.items():
+            # dominant emotion is mentioned separately as its own sentence.
+            if key != "dominant_emotion":
+                result += f" '{key}': {value},"
+        # Replace last comma with point. If index is -1, no commas found.
+        last_comma_index = result.rfind(",")
+        if last_comma_index != -1:
+            result = result[:last_comma_index] + '.' + result[last_comma_index + 1:]
+
+        result += f" The dominant emotion is {response['dominant_emotion']}."
+
+    return result
+
+
+
+if __name__ == "__main__":
+    app.run(debug=True,host="0.0.0.0", port=5010)

testchild.py

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+## adding a new file
+print("inside child branch")