Reorganizado proyecto

.gitmodules

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+[submodule "final_project"]
+	path = final_project
+	url = https://github.com/rayoconico/F-Quinteros-final-project-emb-ai.git

.theia/launch.json

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+{
+  // Use IntelliSense to learn about possible attributes.
+  // Hover to view descriptions of existing attributes.
+  "version": "0.2.0",
+<<<<<<< HEAD
+  "configurations": []
+=======
+  "configurations": [
+
+  ]
+>>>>>>> fe5311dee5d7aa23c2b5ffb2543e29c634742265
+}

.theia/settings.json

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+{
+    "php.suggest.basic": false,
+    "java.errors.incompleteClasspath.severity": "ignore",
+    "security.workspace.trust.enabled": true,
+    "security.workspace.trust.startupPrompt": "never",
+    "security.workspace.trust.untrustedFiles": "open",
+    "liveServer.settings.donotShowInfoMsg": false,
+}

final_project/EmotionDetection/__init__.py

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+from .emotion_detection import emotion_detector

final_project/EmotionDetection/emotion_detection.py

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+import requests
+import json
+
+def emotion_detector(text_to_analyze):
+    # Si el texto está vacío o tiene solo espacios, devolvemos un diccionario con None
+    if not text_to_analyze.strip():
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }
+
+    # El resto del código sigue igual:
+    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, json=input_json, headers=headers)
+
+    if response.status_code == 200:
+        response_data = response.json()
+        emotions = response_data['emotionPredictions'][0]['emotion']
+        dominant_emotion = max(emotions, key=emotions.get)
+
+        return {
+            'anger': emotions.get('anger'),
+            'disgust': emotions.get('disgust'),
+            'fear': emotions.get('fear'),
+            'joy': emotions.get('joy'),
+            'sadness': emotions.get('sadness'),
+            'dominant_emotion': dominant_emotion
+        }
+    else:
+        return {
+            'anger': None,
+            'disgust': None,
+            'fear': None,
+            'joy': None,
+            'sadness': None,
+            'dominant_emotion': None
+        }

final_project/server.py

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+"""
+server.py
+
+Este archivo contiene el servidor Flask que maneja las peticiones POST a la ruta '/emotionDetector'.
+Utiliza la función 'emotion_detector' para detectar emociones en un texto dado.
+
+Dependencias:
+    - Flask
+    - EmotionDetection (función emotion_detector)
+    - requests (para manejar errores de conexión)
+
+Funciones:
+    - detect_emotion: Maneja las peticiones POST y devuelve las emociones detectadas.
+"""
+from flask import Flask, request, jsonify
+from EmotionDetection.emotion_detection import emotion_detector
+import requests  # Aquí importamos 'requests'
+
+app = Flask(__name__)
+
+@app.route("/emotionDetector", methods=["POST"])
+def detect_emotion():
+    """
+    Recibe un texto por POST y detecta la emoción dominante utilizando el modelo de Watson NLP.
+
+    Retorna:
+        dict: Un diccionario con las emociones detectadas y la emoción dominante.
+    """
+    try:
+        data = request.get_json()
+        text_to_analyze = data.get("text", "")
+
+        if not text_to_analyze.strip():
+            return jsonify(message="¡Texto inválido! ¡Por favor, intenta de nuevo!"), 400
+
+        emotions = emotion_detector(text_to_analyze)
+
+        if emotions["dominant_emotion"] is None:
+            return jsonify(message="¡Texto inválido! ¡Por favor, intenta de nuevo!"), 400
+
+        response_message = (
+            f"Para la declaración dada, la respuesta del sistema es "
+            f"'anger': {emotions['anger']}, "
+            f"'disgust': {emotions['disgust']}, "
+            f"'fear': {emotions['fear']}, "
+            f"'joy': {emotions['joy']}, "
+            f"'sadness': {emotions['sadness']}. "
+            f"La emoción dominante es {emotions['dominant_emotion']}."
+        )
+
+        return jsonify(message=response_message)
+
+    except (requests.exceptions.RequestException, ValueError) as e:
+        # Captura excepciones más específicas
+        print(f"Error: {e}")
+        return jsonify(message="¡Ocurrió un error en el servidor!"), 500
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000, debug=True)

final_project/setup.py

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+from setuptools import setup, find_packages
+
+setup(
+    name='EmotionDetection',
+    version='0.1',
+    packages=find_packages(),
+    install_requires=[
+        "requests",
+        "Flask",
+    ],
+)

final_project/test_emotion_detection.py

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+import unittest
+from EmotionDetection.emotion_detection import emotion_detector
+
+class TestEmotionDetection(unittest.TestCase):
+
+    def test_joy_emotion(self):
+        """Test that joy is correctly detected as the dominant emotion"""
+        result = emotion_detector("I am glad this happened")
+        print(result)
+        self.assertEqual(result["dominant_emotion"], "joy")
+
+    def test_anger_emotion(self):
+        """Test that anger is correctly detected as the dominant emotion"""
+        result = emotion_detector("I am really angry about this")
+        print(result)
+        self.assertEqual(result["dominant_emotion"], "anger")
+
+    def test_disgust_emotion(self):
+        """Test that disgust is correctly detected as the dominant emotion"""
+        result = emotion_detector("I feel disgusted just hearing about this")
+        print(result)
+        self.assertEqual(result["dominant_emotion"], "disgust")
+
+    def test_sadness_emotion(self):
+        """Test that sadness is correctly detected as the dominant emotion"""
+        result = emotion_detector("I am so sad about this")
+        print(result)
+        self.assertEqual(result["dominant_emotion"], "sadness")
+
+    def test_fear_emotion(self):
+        """Test that fear is correctly detected as the dominant emotion"""
+        result = emotion_detector("I am very afraid this will happen")
+        print(result)
+        self.assertEqual(result["dominant_emotion"], "fear")
+
+if __name__ == "__main__":
+    unittest.main()

requirements.txt

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+requests==2.32.3