From 443663b0383af5cf6fa9ce3ff4479975d2b6c9cc Mon Sep 17 00:00:00 2001
From: ASameh14 <ahmedsameh14141@gmail.com>
Date: Mon, 6 Oct 2025 18:09:00 -0400
Subject: [PATCH] Final project completed: emotion detection, Flask web app,
 error handling, static analysis

---
 EmotionDetection/__init__.py          |  1 +
 EmotionDetection/emotion_detection.py | 40 +++++++++++++++++++
 server.py                             | 56 +++++++++++++++++++++++++++
 test_emotion_detection.py             | 27 +++++++++++++
 4 files changed, 124 insertions(+)
 create mode 100644 EmotionDetection/__init__.py
 create mode 100644 EmotionDetection/emotion_detection.py
 create mode 100644 server.py
 create mode 100644 test_emotion_detection.py

diff --git a/EmotionDetection/__init__.py b/EmotionDetection/__init__.py
new file mode 100644
index 000000000..0b7bf8661
--- /dev/null
+++ b/EmotionDetection/__init__.py
@@ -0,0 +1 @@
+from .emotion_detection import emotion_detector
diff --git a/EmotionDetection/emotion_detection.py b/EmotionDetection/emotion_detection.py
new file mode 100644
index 000000000..f73deeab7
--- /dev/null
+++ b/EmotionDetection/emotion_detection.py
@@ -0,0 +1,40 @@
+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    if not text_to_analyze.strip():
+        # Handle blank input (status_code = 400 behavior)
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    # Call Watson NLP API (or your API endpoint)
+    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"}
+    input_json = {"raw_document": {"text": text_to_analyze}}
+
+    response = requests.post(url, headers=headers, json=input_json)
+
+    # Check for 400 Bad Request (invalid or empty)
+    if response.status_code == 400:
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    # Normal successful response
+    response_dict = json.loads(response.text)
+    emotions = response_dict["emotionPredictions"][0]["emotion"]
+    dominant_emotion = max(emotions, key=emotions.get)
+
+    emotions["dominant_emotion"] = dominant_emotion
+    return emotions
diff --git a/server.py b/server.py
new file mode 100644
index 000000000..8eb855470
--- /dev/null
+++ b/server.py
@@ -0,0 +1,56 @@
+"""
+Flask web server for Emotion Detection application.
+Handles text input from the user interface and returns emotion analysis results.
+"""
+
+from flask import Flask, request, render_template
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask(
+    __name__,
+    template_folder="/home/project/Final_project/Flask_Emo/templates",
+    static_folder="/home/project/Final_project/Flask_Emo/static"
+)
+
+
+@app.route("/", methods=["GET"])
+def index():
+    """
+    Render the index page for the web application.
+    """
+    return render_template("index.html")
+
+
+@app.route("/emotionDetector", methods=["GET"])
+def emotion_detection():
+    """
+    Handle emotion detection requests from the web interface.
+
+    Returns:
+        str: A formatted message containing 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 invalid or blank text
+    if result["dominant_emotion"] is None:
+        return "Invalid text! Please try again!"
+
+    # Prepare formatted response
+    response_text = (
+        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_text
+
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000, debug=True)
diff --git a/test_emotion_detection.py b/test_emotion_detection.py
new file mode 100644
index 000000000..fa4d9d463
--- /dev/null
+++ b/test_emotion_detection.py
@@ -0,0 +1,27 @@
+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()