Done all the necessary Changes.

EmotionDetection/__init__.py

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+from .emotion_detection import emotion_detector

EmotionDetection/emotion_detection.py

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+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    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"}
+    data = { "raw_document": { "text": text_to_analyze } }
+
+    response = requests.post(url, json=data, headers=headers)
+
+    if response.status_code == 400:
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    result = json.loads(response.text)
+
+    emotions = result['emotionPredictions'][0]['emotion']
+    anger = emotions['anger']
+    disgust = emotions['disgust']
+    fear = emotions['fear']
+    joy = emotions['joy']
+    sadness = emotions['sadness']
+
+    # Get the dominant emotion
+    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
+    }

README.md

@@ -1 +1,231 @@
-# Repository for final project
+# 🧠 AI-Based Emotion Detection Web Application
+
+A complete AI-powered web application built with Flask and IBM Watson NLP to detect **emotions** from customer feedback text. This project includes **data analysis**, **testing**, **error handling**, **static code analysis**, and **web deployment**.
+
+---
+
+## 📁 Project Structure
+
+```
+final_project/
+│
+├── EmotionDetection/
+│   ├── __init__.py
+│   └── emotion_detection.py
+│
+├── static/
+│   └── mywebscript.js
+│
+├── templates/
+│   └── index.html
+│
+├── server.py
+├── test_emotion_detection.py
+├── README.md
+└── [screenshots...]
+```
+
+---
+
+## 🧩 Tasks Overview & Code
+
+### ✅ Task 1: Fork & Clone
+
+```bash
+git clone https://github.com/YOUR_USERNAME/oaqjp-final-project-emb-ai.git final_project
+cd final_project
+```
+
+---
+
+### ✅ Task 2: Create Emotion Detection Module
+
+**File:** `emotion_detection.py`
+
+```python
+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    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"}
+    data = { "raw_document": { "text": text_to_analyze } }
+    response = requests.post(url, headers=headers, json=data)
+    if response.status_code == 400:
+        return {
+            'anger': None, 'disgust': None, 'fear': None,
+            'joy': None, 'sadness': None, 'dominant_emotion': None
+        }
+    response_json = json.loads(response.text)
+    emotions = response_json['emotionPredictions'][0]['emotion']
+    dominant_emotion = max(emotions, key=emotions.get)
+    return {
+        'anger': emotions['anger'],
+        'disgust': emotions['disgust'],
+        'fear': emotions['fear'],
+        'joy': emotions['joy'],
+        'sadness': emotions['sadness'],
+        'dominant_emotion': dominant_emotion
+    }
+```
+
+---
+
+### ✅ Task 3: Format Output
+
+Already included above in `emotion_detector()` function.
+
+---
+
+### ✅ Task 4: Package as Module
+
+**Folder:** `EmotionDetection/`  
+**File:** `__init__.py`
+
+```python
+from .emotion_detection import emotion_detector
+```
+
+---
+
+### ✅ Task 5: Unit Testing
+
+**File:** `test_emotion_detection.py`
+
+```python
+import unittest
+from EmotionDetection.emotion_detection import emotion_detector
+
+class TestEmotionDetection(unittest.TestCase):
+    def test_joy(self):
+        self.assertEqual(emotion_detector("I am glad this happened")['dominant_emotion'], "joy")
+    def test_anger(self):
+        self.assertEqual(emotion_detector("I am really mad about this")['dominant_emotion'], "anger")
+    def test_disgust(self):
+        self.assertEqual(emotion_detector("I feel disgusted just hearing about this")['dominant_emotion'], "disgust")
+    def test_sadness(self):
+        self.assertEqual(emotion_detector("I am so sad about this")['dominant_emotion'], "sadness")
+    def test_fear(self):
+        self.assertEqual(emotion_detector("I am really afraid that this will happen")['dominant_emotion'], "fear")
+
+if __name__ == '__main__':
+    unittest.main()
+```
+
+Run the tests:
+
+```bash
+python3 test_emotion_detection.py
+```
+
+---
+
+### ✅ Task 6: Web Deployment with Flask
+
+**File:** `server.py`
+
+```python
+from flask import Flask, request, render_template
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask(__name__)
+
+@app.route('/emotionDetector')
+def emotion_detector_route():
+    text = request.args.get('textToAnalyze')
+    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']}, 'disgust': {result['disgust']}, "
+                f"'fear': {result['fear']}, 'joy': {result['joy']} and "
+                f"'sadness': {result['sadness']}. The dominant emotion is "
+                f"{result['dominant_emotion']}.")
+    return response
+
+if __name__ == '__main__':
+    app.run(debug=True)
+```
+
+Run the app:
+
+```bash
+python3 server.py
+```
+
+Access at: [http://localhost:5000](http://localhost:5000)
+
+---
+
+### ✅ Task 7: Error Handling
+
+Handled in `emotion_detector()` (via `status_code == 400`) and in `server.py` (`dominant_emotion == None`).
+
+---
+
+### ✅ Task 8: Static Code Analysis
+
+Run Pylint:
+
+```bash
+pylint server.py
+```
+
+Ensure functions have proper **docstrings** to get a 10/10 score.
+
+---
+
+## 💡 Sample Input/Output
+
+**Input Text:** "I love my life"
+
+**Formatted Output:**
+
+```json
+{
+  "anger": 0.0062,
+  "disgust": 0.0025,
+  "fear": 0.0092,
+  "joy": 0.9680,
+  "sadness": 0.0497,
+  "dominant_emotion": "joy"
+}
+```
+
+**Response Message:**  
+"For the given statement, the system response is 'anger': 0.0062, 'disgust': 0.0025, 'fear': 0.0092, 'joy': 0.9680 and 'sadness': 0.0497. The dominant emotion is joy."
+
+---
+
+## 📸 Screenshots
+
+All screenshots from each task (e.g., `1_folder_structure.png`, `2a_emotion_detection.png`, `6b_deployment_test.png`) are included in the root project directory.
+
+---
+
+## 🧪 Technologies Used
+
+- Python 3.11
+- Flask (Web framework)
+- Watson NLP API (Emotion Detection)
+- Requests (HTTP client)
+- unittest (Testing)
+- pylint (Static Code Analysis)
+
+---
+
+## 👨‍💻 Author
+
+**Souvik Mandal**  
+IBM AI Engineering Final Project  
+GitHub: [Souvik Mandal](https://github.com/AP19110010485)
+
+---
+
+## 🏁 Status
+
+✅ All 8 tasks completed  
+✅ Fully functional and tested  
+✅ 10/10 code quality
+
+🎉 Thank you for visiting the project!

requirements.txt

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+Flask==2.3.2
+requests==2.31.0
+pylint==3.0.2

server.py

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+"""Flask server for Emotion Detection App."""
+
+from flask import Flask, request, render_template
+from EmotionDetection.emotion_detection import emotion_detector
+
+app = Flask(__name__)
+
+@app.route("/")
+def render_index_page():
+    """Renders the main page of the application."""
+    return render_template("index.html")
+
+@app.route("/emotionDetector")
+def analyze_emotion():
+    """Handles emotion analysis request and returns formatted response."""
+    text_to_analyze = request.args.get("textToAnalyze")
+    response = emotion_detector(text_to_analyze)
+
+    if response["dominant_emotion"] is None:
+        return "Invalid text! Please try again!"
+
+    return (
+        f"For the given statement, the system response is "
+        f"'anger': {response['anger']}, "
+        f"'disgust': {response['disgust']}, "
+        f"'fear': {response['fear']}, "
+        f"'joy': {response['joy']} and "
+        f"'sadness': {response['sadness']}. "
+        f"The dominant emotion is {response['dominant_emotion']}."
+    )
+
+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 TestEmotionDetector(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()