Initial Commit

EmotionDetection/__init__.py

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+from . import emotion_detection

EmotionDetection/emotion_detection.py

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+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    """Function to analyze emotions in text using Watson NLP and handle errors properly."""
+
+    if not text_to_analyze.strip():  # Check if input is empty or contains only spaces
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    url = "https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict"
+    headers = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+    payload = {"raw_document": {"text": text_to_analyze}}
+
+    response = requests.post(url, json=payload, headers=headers)
+
+    if response.status_code == 200:
+        formatted_response = json.loads(response.text)
+
+        if 'emotionPredictions' not in formatted_response:
+            return {
+                'anger': None,
+                'disgust': None,
+                'fear': None,
+                'joy': None,
+                'sadness': None,
+                'dominant_emotion': None
+            }
+
+        emotions = formatted_response['emotionPredictions'][0]['emotion']
+
+        return {
+            'anger': emotions.get('anger', 0),
+            'disgust': emotions.get('disgust', 0),
+            'fear': emotions.get('fear', 0),
+            'joy': emotions.get('joy', 0),
+            'sadness': emotions.get('sadness', 0),
+            'dominant_emotion': max(emotions, key=emotions.get)
+        }
+
+    elif response.status_code == 400:  # Handle bad request errors
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    else:
+        return {"error": f"Request failed with status code {response.status_code}"}
+
+# Example usage
+if __name__ == "__main__":
+    text = " "
+    result = emotion_detector(text)
+    print(result)

emotion_detection.py

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+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    """Function to analyze emotions in text using Watson NLP and format the output"""
+
+    url = "https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict"
+    headers = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"}
+    payload = {"raw_document": {"text": text_to_analyze}}
+
+    response = requests.post(url, json=payload, headers=headers)
+
+    if response.status_code == 200:
+        formatted_response = json.loads(response.text)  # Ensure JSON parsing using json library
+
+        if 'emotionPredictions' not in formatted_response:
+            return {"error": "No emotions detected in the response."}
+
+        emotions = formatted_response['emotionPredictions'][0]['emotion']
+
+        return {
+            'anger': emotions.get('anger', 0),
+            'disgust': emotions.get('disgust', 0),
+            'fear': emotions.get('fear', 0),
+            'joy': emotions.get('joy', 0),
+            'sadness': emotions.get('sadness', 0),
+            'dominant_emotion': max(emotions, key=emotions.get)
+        }
+    else:
+        return {"error": f"Request failed with status code {response.status_code}"}
+
+# Example usage
+if __name__ == "__main__":
+    text = "I am so happy I am doing this."
+    result = emotion_detector(text)
+    print(result)

server.py

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+"""
+Flask server for emotion detection API.
+"""
+
+from flask import Flask, request, jsonify, render_template
+from EmotionDetection import emotion_detection
+
+app = Flask(__name__)
+
+@app.route('/')
+def home():
+    """
+    Renders the home page.
+    """
+    return render_template('index.html')
+
+@app.route('/emotionDetector', methods=['POST'])
+def emotion_detector_api():
+    """
+    Handles POST requests for emotion detection.
+
+    Expects JSON data with a "text" key.
+    Returns a JSON response containing emotion scores
+    and the dominant emotion.
+    """
+    data = request.json
+    text_to_analyze = data.get("text", "").strip()
+
+    if not text_to_analyze:
+        return jsonify({"error": "Invalid text! Please try again!"}), 400
+
+    result = emotion_detection.emotion_detector(text_to_analyze)
+
+    if result["dominant_emotion"] is None:
+        return jsonify({"error": "Invalid text! Please try again!"}), 400
+
+    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']}, "
+        f"and 'sadness': {result['sadness']}. "
+        f"The dominant emotion is {result['dominant_emotion']}."
+    )
+
+    return jsonify({"response": response_text, "data": result})
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=7000, debug=True)

static/mywebscript.js

@@ -1,12 +1,24 @@
-let RunSentimentAnalysis = ()=>{
-    textToAnalyze = document.getElementById("textToAnalyze").value;
+let RunSentimentAnalysis = () => {
+    let textToAnalyze = document.getElementById("textToAnalyze").value.trim();
 
     let xhttp = new XMLHttpRequest();
     xhttp.onreadystatechange = function() {
-        if (this.readyState == 4 && this.status == 200) {
-            document.getElementById("system_response").innerHTML = xhttp.responseText;
+        if (this.readyState == 4) {
+            let response = JSON.parse(this.responseText);
+
+            if (this.status == 200) {
+                document.getElementById("system_response").innerHTML = response.response;
+            } else if (this.status == 400) {  // Handling empty input case
+                document.getElementById("system_response").innerHTML = response.error;
+            } else {
+                document.getElementById("system_response").innerHTML = "An unexpected error occurred.";
+            }
         }
     };
-    xhttp.open("GET", "emotionDetector?textToAnalyze"+"="+textToAnalyze, true);
-    xhttp.send();
-}
+
+    xhttp.open("POST", "/emotionDetector", true);
+    xhttp.setRequestHeader("Content-Type", "application/json");
+
+    let data = JSON.stringify({ text: textToAnalyze });
+    xhttp.send(data);
+};

test_emotion_detection.py

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