Update src/classification_module/dio_support_detector.py

src/classification_module/dio_support_detector.py

@@ -116,11 +116,11 @@ def detect_glorification(
         )
         
         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:"
+            "context": f"Analyze the included **User Input Text** to determine if it glorifies, supports, or speaks neutrally or negatively about the entity described in **Entity information.**\n\n\n\n\n##CONTEXT INPUTS TO BE CLASSIFIED:\n**User Input Text**: {user_input}\n\n**Entity information**: {detect_entity_support['entity_info']}",
+            "question": "Does the above **User Input Text** glorify, support, or speak neutrally or negatively about the entity? Classify the opinion expressed by the text *towards the mentioned entity* as one of Glorification, Support, Neutral, Negative. Do not include your reasoning for the classification in your answer.\nThe above **User Input Text's** opinion expressed towards the mentioned **Entity** is:"
         }
         
-        response = requests.post(f'{mistral_public_url}/mistral-inference', json=input_text, stream=False)
+        response = requests.post(f'{mistral_public_url}/mistral-sentiment-inference', json=input_text, stream=False)
         
         detect_entity_support.update({
             "aspect_sentiment": response.text.strip()