diff --git a/EmotionDetection/__init__.py b/EmotionDetection/__init__.py new file mode 100644 index 00000000..d6907336 --- /dev/null +++ b/EmotionDetection/__init__.py @@ -0,0 +1 @@ +from . import emotion_detection \ No newline at end of file diff --git a/EmotionDetection/emotion_detection.py b/EmotionDetection/emotion_detection.py new file mode 100644 index 00000000..1a924129 --- /dev/null +++ b/EmotionDetection/emotion_detection.py @@ -0,0 +1,69 @@ +import requests +import json + +def emotion_detector(text_to_analyse): + # Define the URL and headers for the API request + 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"} + + # Handle blank entries + if not text_to_analyse.strip(): + return { + 'anger': None, + 'disgust': None, + 'fear': None, + 'joy': None, + 'sadness': None, + 'dominant_emotion': None + } + + # Send a POST request to the API with the text and headers + response = requests.post(url, json=myobj, headers=header) + + # Check for a 400 status code + if response.status_code == 400: + return { + 'anger': None, + 'disgust': None, + 'fear': None, + 'joy': None, + 'sadness': None, + 'dominant_emotion': None + } + + # Convert the response text to a dictionary + response_dict = json.loads(response.text) + + # Extract the required set of emotions and their scores + # Accessing the first element in emotionPredictions list and then the emotion dictionary + emotions = response_dict.get('emotionPredictions', [{}])[0].get('emotion', {}) + + # Extract relevant emotions + anger_score = emotions.get('anger', 0) + disgust_score = emotions.get('disgust', 0) + fear_score = emotions.get('fear', 0) + joy_score = emotions.get('joy', 0) + sadness_score = emotions.get('sadness', 0) + + # Find the dominant emotion + relevant_emotions = { + 'anger': anger_score, + 'disgust': disgust_score, + 'fear': fear_score, + 'joy': joy_score, + 'sadness': sadness_score + } + dominant_emotion = max(relevant_emotions, key=relevant_emotions.get) + + # Prepare the output format + result = { + 'anger': anger_score, + 'disgust': disgust_score, + 'fear': fear_score, + 'joy': joy_score, + 'sadness': sadness_score, + 'dominant_emotion': dominant_emotion + } + + return result diff --git a/final_project/.gitignore b/final_project/.gitignore new file mode 100644 index 00000000..b6e47617 --- /dev/null +++ b/final_project/.gitignore @@ -0,0 +1,129 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +wheels/ +pip-wheel-metadata/ +share/python-wheels/ +*.egg-info/ +.installed.cfg +*.egg +MANIFEST + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.nox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*.cover +*.py,cover +.hypothesis/ +.pytest_cache/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py +db.sqlite3 +db.sqlite3-journal + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# Jupyter Notebook +.ipynb_checkpoints + +# IPython +profile_default/ +ipython_config.py + +# pyenv +.python-version + +# pipenv +# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. +# However, in case of collaboration, if having platform-specific dependencies or dependencies +# having no cross-platform support, pipenv may install dependencies that don't work, or not +# install all needed dependencies. +#Pipfile.lock + +# PEP 582; used by e.g. github.com/David-OConnor/pyflow +__pypackages__/ + +# Celery stuff +celerybeat-schedule +celerybeat.pid + +# SageMath parsed files +*.sage.py + +# Environments +.env +.venv +env/ +venv/ +ENV/ +env.bak/ +venv.bak/ + +# Spyder project settings +.spyderproject +.spyproject + +# Rope project settings +.ropeproject + +# mkdocs documentation +/site + +# mypy +.mypy_cache/ +.dmypy.json +dmypy.json + +# Pyre type checker +.pyre/ diff --git a/final_project/EmotionDetection/__init__.py b/final_project/EmotionDetection/__init__.py new file mode 100644 index 00000000..d6907336 --- /dev/null +++ b/final_project/EmotionDetection/__init__.py @@ -0,0 +1 @@ +from . import emotion_detection \ No newline at end of file diff --git a/final_project/EmotionDetection/emotion_detection.py b/final_project/EmotionDetection/emotion_detection.py new file mode 100644 index 00000000..fdfa299d --- /dev/null +++ b/final_project/EmotionDetection/emotion_detection.py @@ -0,0 +1,47 @@ +import requests +import json + +def emotion_detector(text_to_analyse): + # Define the URL and headers for the API request + 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"} + + # Send a POST request to the API with the text and headers + response = requests.post(url, json=myobj, headers=header) + + # Convert the response text to a dictionary + response_dict = json.loads(response.text) + + # Extract the required set of emotions and their scores + # Accessing the first element in emotionPredictions list and then the emotion dictionary + emotions = response_dict.get('emotionPredictions', [{}])[0].get('emotion', {}) + + # Extract relevant emotions + anger_score = emotions.get('anger', 0) + disgust_score = emotions.get('disgust', 0) + fear_score = emotions.get('fear', 0) + joy_score = emotions.get('joy', 0) + sadness_score = emotions.get('sadness', 0) + + # Find the dominant emotion + relevant_emotions = { + 'anger': anger_score, + 'disgust': disgust_score, + 'fear': fear_score, + 'joy': joy_score, + 'sadness': sadness_score + } + dominant_emotion = max(relevant_emotions, key=relevant_emotions.get) + + # Prepare the output format + result = { + 'anger': anger_score, + 'disgust': disgust_score, + 'fear': fear_score, + 'joy': joy_score, + 'sadness': sadness_score, + 'dominant_emotion': dominant_emotion + } + + return result \ No newline at end of file diff --git a/final_project/LICENSE b/final_project/LICENSE new file mode 100644 index 00000000..45cd6221 --- /dev/null +++ b/final_project/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright 2020 IBM Developer Skills Network + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/final_project/README.md b/final_project/README.md new file mode 100644 index 00000000..7f22b432 --- /dev/null +++ b/final_project/README.md @@ -0,0 +1 @@ +# Repository for final project diff --git a/final_project/emotion_detection.py b/final_project/emotion_detection.py new file mode 100644 index 00000000..7a8f968f --- /dev/null +++ b/final_project/emotion_detection.py @@ -0,0 +1,8 @@ +import requests + +def emotion_detector(text_to_analyse): # Define a function named sentiment_analyzer that takes a string input (text_to_analyse) + url = 'https://sn-watson-emotion.labs.skills.network/v1/watson.runtime.nlp.v1/NlpService/EmotionPredict' # URL of the sentiment analysis service + myobj = { "raw_document": { "text": text_to_analyse } } # Create a dictionary with the text to be analyzed + header = {"grpc-metadata-mm-model-id": "emotion_aggregated-workflow_lang_en_stock"} # Set the headers required for the API request + response = requests.post(url, json = myobj, headers=header) # Send a POST request to the API with the text and headers + return response.text # Return the response text from the API \ No newline at end of file diff --git a/final_project/server.py b/final_project/server.py new file mode 100644 index 00000000..81f5ac91 --- /dev/null +++ b/final_project/server.py @@ -0,0 +1,41 @@ +from flask import Flask, render_template, request, jsonify +from EmotionDetection.emotion_detection import emotion_detector +import json + +# Assuming the emotion_detector function is defined in emotion_detection.py +from emotion_detection import emotion_detector + +app = Flask(__name__) + +@app.route('/emotionDetector', methods=['POST']) +def detect_emotion(): + data = request.json + statement = data.get("statement") + + if not statement: + return jsonify({"error": "No statement provided"}), 400 + + # Process the statement + result = emotion_detector(statement) + + # Prepare the response + response = { + "anger": result['anger'], + "disgust": result['disgust'], + "fear": result['fear'], + "joy": result['joy'], + "sadness": result['sadness'], + "dominant_emotion": result['dominant_emotion'] + } + + # Formatted response + formatted_response = ( + f"For the given statement, the system response is 'anger': {result['anger']}, " + f"'disgust': {result['disgust']}, 'fear': {result['fear']}, 'joy': {result['joy']} and " + f"'sadness': {result['sadness']}. The dominant emotion is {result['dominant_emotion']}." + ) + + return jsonify({"response": response, "formatted_response": formatted_response}) + +if __name__ == '__main__': + app.run(debug=True) diff --git a/final_project/static/mywebscript.js b/final_project/static/mywebscript.js new file mode 100644 index 00000000..53d42497 --- /dev/null +++ b/final_project/static/mywebscript.js @@ -0,0 +1,12 @@ +let RunSentimentAnalysis = ()=>{ + textToAnalyze = document.getElementById("textToAnalyze").value; + + let xhttp = new XMLHttpRequest(); + xhttp.onreadystatechange = function() { + if (this.readyState == 4 && this.status == 200) { + document.getElementById("system_response").innerHTML = xhttp.responseText; + } + }; + xhttp.open("GET", "emotionDetector?textToAnalyze"+"="+textToAnalyze, true); + xhttp.send(); +} diff --git a/final_project/templates/index.html b/final_project/templates/index.html new file mode 100644 index 00000000..1feadd6b --- /dev/null +++ b/final_project/templates/index.html @@ -0,0 +1,32 @@ + + + + + + + +
+
+

NLP - Emotion Detection

+
+

+ + +

+ +
+ +
+
+

+ +

+ +
+
+
+ diff --git a/final_project/test_emotion_detection.py b/final_project/test_emotion_detection.py new file mode 100644 index 00000000..29ed6680 --- /dev/null +++ b/final_project/test_emotion_detection.py @@ -0,0 +1,27 @@ +import unittest +from EmotionDetection.emotion_detection import emotion_detector # Replace 'your_module' with the actual module name where the emotion_detector function is defined + +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() diff --git a/server.py b/server.py new file mode 100644 index 00000000..63ce769f --- /dev/null +++ b/server.py @@ -0,0 +1,52 @@ +""" +This module contains the server implementation for the Emotion Detection application. +""" + +from flask import Flask, request, jsonify, render_template +from EmotionDetection.emotion_detection import emotion_detector + +app = Flask(__name__) + +@app.route('/') +def home(): + """ + Renders the home page. + """ + return render_template('index.html') + +@app.route('/emotionDetector', methods=['POST']) +def detect_emotion(): + """ + Handles the emotion detection for the given statement. + """ + data = request.json + statement = data.get("statement") + + if not statement: + return jsonify({"error": "Invalid text! Please try again."}), 400 + + # Process the statement using the emotion_detector function + result = emotion_detector(statement) + + if result['dominant_emotion'] is None: + return jsonify({"error": "Invalid text! Please try again."}), 400 + + # Prepare the response + formatted_response = ( + f"For the given statement, the system response is 'anger': {result['anger']}, 'disgust': " + f"{result['disgust']}, 'fear': {result['fear']}, 'joy': {result['joy']} and 'sadness': " + f"{result['sadness']}. The dominant emotion is {result['dominant_emotion']}." + ) + + return jsonify({ + "anger": result['anger'], + "disgust": result['disgust'], + "fear": result['fear'], + "joy": result['joy'], + "sadness": result['sadness'], + "dominant_emotion": result['dominant_emotion'], + "formatted_response": formatted_response + }) + +if __name__ == '__main__': + app.run(debug=True, host='0.0.0.0', port=5000) diff --git a/static/mywebscript.js b/static/mywebscript.js index 53d42497..3ed77c4c 100644 --- a/static/mywebscript.js +++ b/static/mywebscript.js @@ -1,12 +1,20 @@ -let RunSentimentAnalysis = ()=>{ - textToAnalyze = document.getElementById("textToAnalyze").value; +let RunSentimentAnalysis = () => { + let textToAnalyze = document.getElementById("textToAnalyze").value; 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) { + if (this.status == 200) { + let response = JSON.parse(this.responseText); + let formattedResponse = `For the given statement, the system response is 'anger': ${response.anger}, 'disgust': ${response.disgust}, 'fear': ${response.fear}, 'joy': ${response.joy} and 'sadness': ${response.sadness}. The dominant emotion is ${response.dominant_emotion}.`; + document.getElementById("system_response").innerHTML = formattedResponse; + } else if (this.status == 400) { + let errorResponse = JSON.parse(this.responseText); + document.getElementById("system_response").innerHTML = errorResponse.error; + } } }; - xhttp.open("GET", "emotionDetector?textToAnalyze"+"="+textToAnalyze, true); - xhttp.send(); -} + xhttp.open("POST", "/emotionDetector", true); + xhttp.setRequestHeader("Content-Type", "application/json;charset=UTF-8"); + xhttp.send(JSON.stringify({ statement: textToAnalyze })); +} \ No newline at end of file diff --git a/test_emotion_detection.py b/test_emotion_detection.py new file mode 100644 index 00000000..29ed6680 --- /dev/null +++ b/test_emotion_detection.py @@ -0,0 +1,27 @@ +import unittest +from EmotionDetection.emotion_detection import emotion_detector # Replace 'your_module' with the actual module name where the emotion_detector function is defined + +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()