Master

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_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

final_project/.gitignore

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+# 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/

final_project/EmotionDetection/__init__.py

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+from . import emotion_detection

final_project/EmotionDetection/emotion_detection.py

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+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