Update src/classification_module/dio_support_detector.py

src/classification_module/dio_support_detector.py

@@ -1,135 +1,135 @@
-import ast
-from hamilton.function_modifiers import extract_fields
-from types import ModuleType
-import math
-from langchain.llms import OpenAI
-import requests
-import requests.models
-from knowledge_service.knowledge_retrieval import get_information
-import traceback
-
-prompt = """
-
-Envy:
-
-Contempt: "What the hell is wrong with this country? Why is the official page of police in NRW tweeting in Arabic? Are they seeking to appease the barbaric, Muslim, rapist hordes of men?"
-
-Humiliation: 
-
-Pride:
-
-Elation: 
-
-"""
-
-def detect_entity_support(user_input: str, analyze_affect: dict, ner_public_url: str) -> dict:
-    """
-    Detect whether the user input text is glorifying or supporting an entity
-    from the Dangerous Individuals & Organizations KG.
-    """
-    input_text = {
-        "text": user_input,
-        "entity_type": "organization"
-    }
-
-    # Function to fetch the response from the Mistral model
-    def fetch_ner_entities(input_text):
-        response: requests.models.Response = requests.post(f'{ner_public_url}/universal-ner', json=input_text, stream=False)
-        response.raise_for_status()
-        result = response.json()
-        print(result)
-        if result.get('ner_output'):
-            output = result.get('ner_output').strip()
-            try:
-                output = ast.literal_eval(output)
-            except:
-                traceback.print_exc()
-                return []
-            
-            return output
-        return []
-
-    def fetch_ner_orgs(user_input):
-        input_text = {
-            "text": user_input,
-            "entity_type": "organization"
-        }
-        return fetch_ner_entities(input_text)
-    
-    def fetch_ner_persons(user_input):
-        input_text = {
-            "text": user_input,
-            "entity_type": "person"
-        }
-        return fetch_ner_entities(input_text)
-        
-    # Fetch the entities
-    extracted_entities = fetch_ner_orgs(user_input)
-    extracted_entities.extend(fetch_ner_persons(user_input))
-    
-    for entity in extracted_entities:
-        entity = entity.strip()
-        entity_info = get_information(entity, "dangerous_organizations")
-        if entity_info:
-            analyze_affect.update({
-                "entity_detected": True,
-                "entity_name": entity,
-                "entity_info": entity_info
-            })
-            return analyze_affect
-        entity_info = get_information(entity, 'dangerous_individuals')
-        if entity_info:
-            analyze_affect.update({
-                "entity_detected": True,
-                "entity_name": entity,
-                "entity_info": entity_info
-            })
-            return analyze_affect
-    analyze_affect['entity_detected'] = False
-    return analyze_affect
-
-@extract_fields(
-    dict(
-        extremism_detected=bool,
-        ideology=str,
-        type_label=str,
-        entity_detected=bool,
-        entity_name=str,
-        entity_info=dict,
-        aspect_sentiment=str
-    )
-)
-def detect_glorification(
-    user_input: str,
-    detect_entity_support: dict,
-    mistral_public_url: str
-) -> dict:
-    """
-    Analyze the sentiment of the input text and determine if it glorifies or supports an entity.
-    """
-    if detect_entity_support["entity_detected"]:
-        prompt = (
-            f"Analyze the following text to determine if it glorifies or supports the following entity:\n\n"
-            f"Entity: {detect_entity_support['entity_name']}\n\n"
-            f"Text: '{user_input}'\n\n"
-            "The answer should be one of the following: Glorifying, Supporting, Neutral, Negative."
-        )
-        
-        input_text = {
-            "context": f"User input text: {user_input}\n\nEntity information: {detect_entity_support['entity_info']}",
-            "question": "Does the text glorify, support, or speak neutrally or negatively about the entity? Classify the sentiment as one of Glorifying, Supporting, Neutral, Negative:"
-        }
-        
-        response = requests.post(f'{mistral_public_url}/mistral-inference', json=input_text, stream=False)
-        
-        detect_entity_support.update({
-            "aspect_sentiment": response.text.strip()
-        })
-        return dict(
-            **detect_entity_support
-        )
-    
-    detect_entity_support["aspect_sentiment"] = "None"
-    return dict(
-            **detect_entity_support
+import ast
+from hamilton.function_modifiers import extract_fields
+from types import ModuleType
+import math
+from langchain.llms import OpenAI
+import requests
+import requests.models
+from knowledge_service.knowledge_retrieval import get_information
+import traceback
+
+prompt = """
+
+Envy:
+
+Contempt: "What the hell is wrong with this country? Why is the official page of police in NRW tweeting in Arabic? Are they seeking to appease the barbaric, Muslim, rapist hordes of men?"
+
+Humiliation: 
+
+Pride:
+
+Elation: 
+
+"""
+
+def detect_entity_support(user_input: str, analyze_affect: dict, ner_public_url: str) -> dict:
+    """
+    Detect whether the user input text is glorifying or supporting an entity
+    from the Dangerous Individuals & Organizations KG.
+    """
+    input_text = {
+        "text": user_input,
+        "entity_type": "organization"
+    }
+
+    # Function to fetch the response from the Mistral model
+    def fetch_ner_entities(input_text):
+        response: requests.models.Response = requests.post(f'{ner_public_url}/universal-ner', json=input_text, stream=False)
+        response.raise_for_status()
+        result = response.json()
+        print(result)
+        if result.get('ner_output'):
+            output = result.get('ner_output').strip()
+            try:
+                output = ast.literal_eval(output)
+            except:
+                traceback.print_exc()
+                return []
+            
+            return output
+        return []
+
+    def fetch_ner_orgs(user_input):
+        input_text = {
+            "text": user_input,
+            "entity_type": "organization"
+        }
+        return fetch_ner_entities(input_text)
+    
+    def fetch_ner_persons(user_input):
+        input_text = {
+            "text": user_input,
+            "entity_type": "person"
+        }
+        return fetch_ner_entities(input_text)
+        
+    # Fetch the entities
+    extracted_entities = fetch_ner_orgs(user_input)
+    extracted_entities.extend(fetch_ner_persons(user_input))
+    
+    for entity in extracted_entities:
+        entity = entity.strip()
+        entity_info = get_information(entity, "dangerous_organizations")
+        if entity_info:
+            analyze_affect.update({
+                "entity_detected": True,
+                "entity_name": entity,
+                "entity_info": entity_info
+            })
+            return analyze_affect
+        entity_info = get_information(entity, 'dangerous_individuals')
+        if entity_info:
+            analyze_affect.update({
+                "entity_detected": True,
+                "entity_name": entity,
+                "entity_info": entity_info
+            })
+            return analyze_affect
+    analyze_affect['entity_detected'] = False
+    return analyze_affect
+
+@extract_fields(
+    dict(
+        extremism_detected=bool,
+        ideology=str,
+        type_label=str,
+        entity_detected=bool,
+        entity_name=str,
+        entity_info=dict,
+        aspect_sentiment=str
+    )
+)
+def detect_glorification(
+    user_input: str,
+    detect_entity_support: dict,
+    mistral_public_url: str
+) -> dict:
+    """
+    Analyze the sentiment of the input text and determine if it glorifies or supports an entity.
+    """
+    if detect_entity_support["entity_detected"]:
+        prompt = (
+            f"Analyze the following text to determine if it glorifies or supports the following entity:\n\n"
+            f"Entity: {detect_entity_support['entity_name']}\n\n"
+            f"Text: '{user_input}'\n\n"
+            "The answer should be one of the following: Glorifying, Supporting, Neutral, Negative."
+        )
+        
+        input_text = {
+            "context": f"Analyze the following text to determine if it glorifies, supports, or speaks neutrally or negatively about the following entity:\n\nUser input text: {user_input}\n\nEntity information: {detect_entity_support['entity_info']}",
+            "question": "Does the text glorify, support, or speak neutrally or negatively about the entity? Classify the opinion sentiment expressed by the text *towards the mentioned entity* as one of Glorifying, Supporting, Neutral, Negative:"
+        }
+        
+        response = requests.post(f'{mistral_public_url}/mistral-inference', json=input_text, stream=False)
+        
+        detect_entity_support.update({
+            "aspect_sentiment": response.text.strip()
+        })
+        return dict(
+            **detect_entity_support
+        )
+    
+    detect_entity_support["aspect_sentiment"] = "None"
+    return dict(
+            **detect_entity_support
         )