Emotionallity Detection update

classifiers/text_analysis/emotionality_detection/__init__.py

@@ -1,35 +1,36 @@
+import requests
+from extractors.util.spacy import SpacySingleton
 from pydantic import BaseModel
-from LeXmo import LeXmo
 
 INPUT_EXAMPLE = {
-    "text": """As Harry went inside the Chamber of Secrets, he discovered the Basilisk's layer. Before him stood Tom
-            Riddle, with his wand. Harry was numb for a second as if he had seen a ghost. Moments later the giant 
-            snake attacked Harry but fortunately, Harry dodged and ran into one of the sewer lines while the serpent 
-            followed. The Basilisk couldn't be killed with bare hands but only with a worthy weapon."""
+      "text": "I did not know that you were coming! I am very glad to see you. If you would tell me sooner, I would have baked some cookies, though.",
+      "apiKey": "<API_KEY_GOES_HERE>"
 }
 
-
 class EmotionalityDetectionModel(BaseModel):
-    text: str
+      apiKey: str
+      text: str 
 
-    class Config:
-        schema_example = {"example": INPUT_EXAMPLE}
+      class Config:
+            schema_example = {"example": INPUT_EXAMPLE}
 
+def emotionality_detection(req: EmotionalityDetectionModel):
+      """BERT model for emotion detection"""
+      headers = {"Authorization": f"Bearer {req.apiKey}"}
+      data = {"inputs": req.text}
+      try: 
+            response = requests.post("https://api-inference.huggingface.co/models/j-hartmann/emotion-english-distilroberta-base", headers=headers, json=data)
+            response_json = response.json()
+            ner_positions = []
 
-def emotionality_detection(request: EmotionalityDetectionModel):
-    """Fetches emotions from a given text"""
+            nlp = SpacySingleton.get_nlp("en_core_web_sm")
+            doc = nlp(req.text)
 
-    text = request.text
-    try:
-        emo = LeXmo.LeXmo(text)
-        del emo["text"]
-        del emo["positive"]
-        del emo["negative"]
-        unique = dict(zip(emo.values(), emo.keys()))
-        if len(unique) == 1:
-            return "Cannot determine emotion"
-        else:
-            emo = max(emo, key=emo.get)
-            return {"emotion": emo}
-    except ValueError:
-        return "Valid text required"
+            for item in response_json:
+                  start = item["start"]
+                  end = item["end"]
+                  span = doc.char_span(start, end, alignment_mode="expand")
+                  ner_positions.append((item["entity_group"], span.start, span.end))
+            return {"entities": ner_positions}
+      except Exception as e: 
+            return f"That didn't work. Did you provide a valid API key? Go error: {e} and message: {response_json}"

classifiers/text_analysis/emotionality_detection/code_snippet_common.md

@@ -1,26 +1,38 @@
 ```python
-from LeXmo import LeXmo
 
-def emotionality_detection(text:str) -> str:
-    """
-    @param text: text to check
-    @return: either 'anger', 'fear', 'anticipation', 'trust', 'surprise', 'sadness', 'joy' or 'disgust' depending on the score
-    """
-    emo = LeXmo.LeXmo(text)
-    del emo["text"]
-    del emo["positive"]
-    del emo["negative"]
-    emo = max(emo, key=emo.get)
-    return emo
-
+import requests
+import spacy
+
+def emotionality_detection(text, api_key):
+      headers = {"Authorization": f"Bearer {api_key}"}
+      data = {"inputs": text}
+      try: 
+            response = requests.post("https://api-inference.huggingface.co/models/j-hartmann/emotion-english-distilroberta-base", headers=headers, json=data)
+            response_json = response.json()
+            ner_positions = []
+
+            nlp = spacy.load("en_core_web_sm")
+            doc = nlp(text)
+
+            for item in response_json:
+                  start = item["start"]
+                  end = item["end"]
+                  span = doc.char_span(start, end, alignment_mode="expand")
+                  ner_positions.append((item["entity_group"], span.start, span.end))
+            return ner_positions
+      except Exception as e: 
+            return f"That didn't work. Did you provide a valid API key? Go error: {e} and message: {response_json}"
+
 # ↑ necessary bricks function 
 # -----------------------------------------------------------------------------------------
 # ↓ example implementation 
 
-def example_integration():
-    texts = ["I am scared to continue.", "Oh my goodness it was the best evening ever, hype!"]
-    for text in texts:
-        print(f"\"{text}\" has emotion: {emotionality_detection(text)}")
+def emotionality_detection():
+      hf_api_key = "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"
+      texts = ["What a great day to go to the beach.", "Sorry to hear that. CAn I help you?", "Why the hell would you do that?"]
+      for text in texts:
+            output = emotion_detection(text, api_key=hf_api_key)
+            print(output)
 
 example_integration()
 ```

classifiers/text_analysis/emotionality_detection/code_snippet_refinery.md

@@ -1,16 +1,21 @@
 ```python
-#expects labeling task to have labels ["anger", "fear", "anticipation", "trust", "surprise", "sadness", "joy", "disgust"]
-from LeXmo import LeXmo
+import requests
 
-ATTRIBUTE: str = "text" # only text attributes
+ATTRIBUTE: str = "text"
+API_KEY: str = "<API_KEY_GOES_HERE>"
 
 def emotionality_detection(record):
-    text = record[ATTRIBUTE].text # SpaCy document, hence we need to call .text to get the string
-    emo = LeXmo.LeXmo(text)
-    del emo["text"]
-    del emo["positive"]
-    del emo["negative"]
-    emo = max(emo, key=emo.get)
-
-    return emo 
+      headers = {"Authorization": f"Bearer {API_KEY}"}
+      data = {"inputs": record[ATTRIBUTE].text}
+      try:
+            response = requests.post("https://api-inference.huggingface.co/models/j-hartmann/emotion-english-distilroberta-base", headers=headers, json=data)
+            response_json = response.json()
+            ner_positions = []
+            for item in response_json:
+                  start = item["start"]
+                  end = item["end"]
+                  span = record[ATTRIBUTE].char_span(start, end, alignment_mode="expand")
+                  yield item["entity_group"], span.start, span.end
+      except Exception as e: 
+            return f"That didn't work. Did you provide a valid API key? Go error: {e} and message: {response_json}"
 ```

classifiers/text_analysis/emotionality_detection/config.py

@@ -19,28 +19,31 @@ def get_config():
             "text_analysis",
         ],  # first entry should be parent directory
         # bricks integrator information
-        integrator_inputs={
+              integrator_inputs={
             "name": "emotionality_detection",
             "refineryDataType": RefineryDataType.TEXT.value,
-            "globalComment": "Only for english text.\nWorks best with longer texts since scores for each word are accumulated.",
             "outputs": [
-                "anger",
-                "fear",
-                "anticipation",
-                "trust",
-                "surprise",
-                "sadness",
-                "joy",
-                "disgust",
+                  "anger", 
+                  "disgust",
+                  "fear",
+                  "joy",
+                  "sadness", 
+                  "neutral",
+                  "suprise"
             ],
             "variables": {
-                "ATTRIBUTE": {
-                    "selectionType": SelectionType.CHOICE.value,
-                    "addInfo": [
+                  "API_KEY": {
+                        "selectionType": SelectionType.STRING.value,
+                        "defaultValue": "<API_KEY_GOES_HERE>",
+                        "addInfo": [BricksVariableType.GENERIC_STRING.value],
+                  },
+                  "ATTRIBUTE": {
+                        "selectionType": SelectionType.CHOICE.value,
+                        "addInfo": [
                         BricksVariableType.ATTRIBUTE.value,
                         BricksVariableType.GENERIC_STRING.value,
-                    ],
-                }
+                        ],
+                  },
             },
-        },
-    )
+      },
+)