diff --git a/EmotionDetection/__init__.py b/EmotionDetection/__init__.py
new file mode 100644
index 000000000..d69073368
--- /dev/null
+++ b/EmotionDetection/__init__.py
@@ -0,0 +1 @@
+from . import emotion_detection
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diff --git a/EmotionDetection/emotion_detection.py b/EmotionDetection/emotion_detection.py
new file mode 100644
index 000000000..17a579ad9
--- /dev/null
+++ b/EmotionDetection/emotion_detection.py
@@ -0,0 +1,48 @@
+import json
+import requests
+
+def emotion_detector(text_to_analyse):
+    url = 'https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict'
+    myobj = {"raw_document": {"text": text_to_analyse}}
+    header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+
+    response = requests.post(url, json=myobj, headers=header)
+
+    # Error handling for bad request
+    if response.status_code == 400:
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    response_dict = json.loads(response.text)
+    emotions = response_dict['emotionPredictions'][0]['emotion']
+
+    anger = emotions['anger']
+    disgust = emotions['disgust']
+    fear = emotions['fear']
+    joy = emotions['joy']
+    sadness = emotions['sadness']
+
+    emotion_scores = {
+        'anger': anger,
+        'disgust': disgust,
+        'fear': fear,
+        'joy': joy,
+        'sadness': sadness
+    }
+
+    dominant_emotion = max(emotion_scores, key=emotion_scores.get)
+
+    return {
+        'anger': anger,
+        'disgust': disgust,
+        'fear': fear,
+        'joy': joy,
+        'sadness': sadness,
+        'dominant_emotion': dominant_emotion
+    }
diff --git a/server.py b/server.py
new file mode 100644
index 000000000..0726e77b9
--- /dev/null
+++ b/server.py
@@ -0,0 +1,42 @@
+"""Flask server for Emotion Detection Application."""
+
+from flask import Flask, render_template, request
+from EmotionDetection.emotion_detection import emotion_detector
+
+# Initialize the Flask application
+app = Flask("Emotion Detector")
+
+@app.route("/emotionDetector")
+def emo_detector():
+    """
+    Endpoint to analyze the emotion in a given text.
+    Returns formatted emotion scores and dominant emotion,
+    or an error message for invalid input.
+    """
+    text_to_analyze = request.args.get('textToAnalyze')
+    result = emotion_detector(text_to_analyze)
+
+    # Handle blank input case
+    if result['dominant_emotion'] is None:
+        return "Invalid text! Please try again!"
+
+    response_text = (
+        f"For the given statement, the system response is "
+        f"'anger': {result['anger']}, 'disgust': {result['disgust']}, "
+        f"'fear': {result['fear']}, 'joy': {result['joy']} and 'sadness': {result['sadness']}. "
+        f"The dominant emotion is {result['dominant_emotion']}."
+    )
+
+    return response_text
+
+@app.route("/")
+def render_index_page():
+    """
+    Serves the main HTML page.
+    """
+    return render_template('index.html')
+
+if __name__ == "__main__":
+    # Run the Flask app on localhost at port 5000.
+    app.run(host="0.0.0.0", port=5000)
+    
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diff --git a/test_emotion_detection.py b/test_emotion_detection.py
new file mode 100644
index 000000000..dce38b2ba
--- /dev/null
+++ b/test_emotion_detection.py
@@ -0,0 +1,25 @@
+from EmotionDetection.emotion_detection import emotion_detector
+import unittest
+
+class TestEmotionDetector(unittest.TestCase):
+    def test_emotion_detector(self):
+        
+        result_1 = emotion_detector('I am glad this happened')
+        self.assertEqual(result_1['dominant_emotion'], 'joy')
+    
+        
+        result_2 = emotion_detector('I am really mad about this')
+        self.assertEqual(result_2['dominant_emotion'], 'anger')
+    
+        
+        result_3 = emotion_detector('I feel disgusted just hearing about this')
+        self.assertEqual(result_3['dominant_emotion'], 'disgust')
+
+
+        result_4 = emotion_detector('I am so sad about this')
+        self.assertEqual(result_4['dominant_emotion'], 'sadness')
+
+        result_5 = emotion_detector('I am really afraid that this will happen')
+        self.assertEqual(result_5['dominant_emotion'], 'fear')
+        
+unittest.main()
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