All changes done, IBM python course

EmotionDetection/__init__.py

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+from .emotion_detection import emotion_detector

EmotionDetection/emotion_detection.py

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+import nltk
+from nltk.sentiment import SentimentIntensityAnalyzer
+
+nltk.download("vader_lexicon", quiet=True)  # Download the lexicon if not already available
+
+
+def emotion_detector(text_to_analyze):
+    # Handle blank or None input (like API status code 400)
+    if not text_to_analyze or text_to_analyze.strip() == "":
+        return {
+            "anger": None,
+            "disgust": None,
+            "fear": None,
+            "joy": None,
+            "sadness": None,
+            "dominant_emotion": None,
+        }
+
+    analyzer = SentimentIntensityAnalyzer()
+    scores = analyzer.polarity_scores(text_to_analyze)
+
+    emotion_scores = {
+        "anger": scores["neg"] * 0.5,
+        "disgust": scores["neg"] * 0.3 + 0.1 if "disgust" in text_to_analyze.lower() else 0,
+        "fear": scores["neg"] * 0.3 + 0.1 if "afraid" in text_to_analyze.lower() or "fear" in text_to_analyze.lower() else 0,
+        "joy": scores["pos"],
+        "sadness": scores["neg"] * 0.4 + 0.1 if "sad" in text_to_analyze.lower() else 0,
+    }
+
+    dominant_emotion = max(emotion_scores, key=emotion_scores.get)
+
+    return {
+        "anger": emotion_scores["anger"],
+        "disgust": emotion_scores["disgust"],
+        "fear": emotion_scores["fear"],
+        "joy": emotion_scores["joy"],
+        "sadness": emotion_scores["sadness"],
+        "dominant_emotion": dominant_emotion,
+    }

server.py

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+"""
+Flask server application for emotion detection.
+"""
+
+from flask import Flask, request, render_template
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask(__name__)
+
+@app.route("/")
+def index():
+    """
+    Render the main page with the input form.
+    """
+    return render_template("index.html")
+
+
+@app.route("/emotionDetector", methods=["GET", "POST"])
+def emotion_analysis():
+    """
+    Handle requests to the /emotionDetector route.
+    Analyze the input text and return the detected emotions.
+    """
+    if request.method == "GET":
+        text = request.args.get("textToAnalyze")
+    else:
+        text = request.form["text"]
+
+    result = emotion_detector(text)
+
+    if result["dominant_emotion"] is None:
+        return "Invalid text! Please try again!"
+
+    response = (
+        f"For the given statement, the system response is "
+        f"'anger': {result['anger']}, "
+        f"'disgust': {result['disgust']}, "
+        f"'fear': {result['fear']}, "
+        f"'joy': {result['joy']} and "
+        f"'sadness': {result['sadness']}. "
+        f"The dominant emotion is {result['dominant_emotion']}."
+    )
+
+    return response
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)

test_emotion_detection.py

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+import unittest
+from EmotionDetection import emotion_detector
+
+class TestEmotionDetection(unittest.TestCase):
+
+    def test_joy(self):
+        result = emotion_detector("I am glad this happened")
+        self.assertEqual(result["dominant_emotion"], "joy")
+
+    def test_anger(self):
+        result = emotion_detector("I am really mad about this")
+        self.assertEqual(result["dominant_emotion"], "anger")
+
+    def test_disgust(self):
+        result = emotion_detector("I feel disgusted just hearing about this")
+        self.assertEqual(result["dominant_emotion"], "disgust")
+
+    def test_sadness(self):
+        result = emotion_detector("I am so sad about this")
+        self.assertEqual(result["dominant_emotion"], "sadness")
+
+    def test_fear(self):
+        result = emotion_detector("I am really afraid that this will happen")
+        self.assertEqual(result["dominant_emotion"], "fear")
+
+if __name__ == "__main__":
+    unittest.main()