Update openai_llms.py

openai_llms.py

@@ -1,63 +1,63 @@
-from openai import OpenAI
-from dotenv import load_dotenv
-import os
-
-load_dotenv()
-
-
-class LLMHandler:
-    def __init__(self, model_name="gpt-4o-mini"):
-        """
-        Initializes the LLMHandler with the specified OpenAI model.
-        """
-        self.openai_api_key = os.getenv("OPENAI_API_KEY")
-        if not self.openai_api_key:
-            raise ValueError("OPENAI_API_KEY environment variable not set.")
-
-        # Initialize OpenAI client
-        self.client = OpenAI(api_key=self.openai_api_key)
-        self.model_name = model_name
-
-    def generate_response(self, user_prompt, data):
-        """
-        Generate a concise response using the LLM based on user prompt and data.
-        :param user_prompt: Prompt provided by the user.
-        :param data: Dictionary containing the instance information.
-        :return: Generated response text.
-        """
-        # Refined prompt to handle encoding and formatting
-        prompt = (
-            f"You are a professional AI model tasked with writing personalized invite texts "
-            f"that are concise (less than 40 words), brochure-suitable, and tailored as per the category in the given sample.\n\n"
-            f"Consider the user prompt: {user_prompt}\n\n"
-            f"Details of the individual:\n"
-            f"- Name: {data['Name']}\n"
-            f"- Job Title: {data['Job Title']}\n"
-            f"- Organisation: {data['Organisation']}\n"
-            f"- Area of Interest: {data['Area of Interest']}\n"
-            f"- Category: {data['Category']}\n\n"
-            f"The response **MUST**:\n"
-            f"- Start with 'Hello {data['Name']}'.\n"
-            f"- Be concise, professional, and STRICTLY DO NOT generate invalid characters or encoding errors (e.g. 'SoraVR’s').\n"
-            f"- Use standard English punctuation, such as single quotes (e.g., 'can't', 'it's').\n"
-            f"- STRICTLY Give only one response for the Category the sample belongs to.\n"
-            f"- Do NOT include preambles or unnecessary text.\n\n"
-            f"Return the final response cleanly, without any extraneous symbols or characters."
-        )
-
-        # Query the OpenAI client and return the response
-        completion = self.client.chat.completions.create(
-            model=self.model_name,
-            messages=[
-                {"role": "system", "content": "You are a professional assistant."},
-                {"role": "user", "content": prompt},
-            ]
-        )
-
-        # Extract and clean the generated response
-        response = completion.choices[0].message.content.strip()
-
-        # Optional: Post-process to clean invalid characters
-        #response_cleaned = response.encode('utf-8').decode('utf-8', errors='ignore')
-
-        return response
+from openai import OpenAI
+#from dotenv import load_dotenv
+import os
+
+#load_dotenv()
+
+
+class LLMHandler:
+    def __init__(self, model_name="gpt-4o-mini"):
+        """
+        Initializes the LLMHandler with the specified OpenAI model.
+        """
+        self.openai_api_key = os.getenv("OPENAI_API_KEY")
+        if not self.openai_api_key:
+            raise ValueError("OPENAI_API_KEY environment variable not set.")
+
+        # Initialize OpenAI client
+        self.client = OpenAI(api_key=self.openai_api_key)
+        self.model_name = model_name
+
+    def generate_response(self, user_prompt, data):
+        """
+        Generate a concise response using the LLM based on user prompt and data.
+        :param user_prompt: Prompt provided by the user.
+        :param data: Dictionary containing the instance information.
+        :return: Generated response text.
+        """
+        # Refined prompt to handle encoding and formatting
+        prompt = (
+            f"You are a professional AI model tasked with writing personalized invite texts "
+            f"that are concise (less than 40 words), brochure-suitable, and tailored as per the category in the given sample.\n\n"
+            f"Consider the user prompt: {user_prompt}\n\n"
+            f"Details of the individual:\n"
+            f"- Name: {data['Name']}\n"
+            f"- Job Title: {data['Job Title']}\n"
+            f"- Organisation: {data['Organisation']}\n"
+            f"- Area of Interest: {data['Area of Interest']}\n"
+            f"- Category: {data['Category']}\n\n"
+            f"The response **MUST**:\n"
+            f"- Start with 'Hello {data['Name']}'.\n"
+            f"- Be concise, professional, and STRICTLY DO NOT generate invalid characters or encoding errors (e.g. 'SoraVR’s').\n"
+            f"- Use standard English punctuation, such as single quotes (e.g., 'can't', 'it's').\n"
+            f"- STRICTLY Give only one response for the Category the sample belongs to.\n"
+            f"- Do NOT include preambles or unnecessary text.\n\n"
+            f"Return the final response cleanly, without any extraneous symbols or characters."
+        )
+
+        # Query the OpenAI client and return the response
+        completion = self.client.chat.completions.create(
+            model=self.model_name,
+            messages=[
+                {"role": "system", "content": "You are a professional assistant."},
+                {"role": "user", "content": prompt},
+            ]
+        )
+
+        # Extract and clean the generated response
+        response = completion.choices[0].message.content.strip()
+
+        # Optional: Post-process to clean invalid characters
+        #response_cleaned = response.encode('utf-8').decode('utf-8', errors='ignore')
+
+        return response