change from gpt3.5 to got 4o mini

src/summarize_text.py

@@ -31,8 +31,12 @@
 # from langchain.chains import LLMChain
 # from langchain.prompts import PromptTemplate
 # from ctransformers import AutoModelForCausalLM, AutoTokenizer
-from langchain_openai import OpenAI
+from langchain_openai import ChatOpenAI
+
 from datetime import datetime
+from langchain_core.prompts import PromptTemplate
+from langchain_core.output_parsers import CommaSeparatedListOutputParser
+
 # from langchain_community.embeddings import HuggingFaceEmbeddings
 from src.Utils.utils import convert_m4a_to_mp3, SpellCheck
 
@@ -44,31 +48,42 @@ def __init__(self, template_path:str = 'config/seperate_task_template.txt', ):
         self.template_path = template_path
         with open(self.template_path, 'r') as file:
             self.template = file.read()
-        self.load_llm()
-        # self.prompt = PromptTemplate(template=self.template, input_variables=['date', 'question'])
-        # self.llm_chain = LLMChain(prompt=self.prompt, llm=self.llm)
+
+        self.OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+        self.llm = self.load_llm()
+        self.output_parser = CommaSeparatedListOutputParser()
 
     def load_llm(self):
-        OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
-        self.llm = OpenAI(model="gpt-3.5-turbo-instruct",
-                          openai_api_key=OPENAI_API_KEY, 
-                          max_tokens = 512,
-                          temperature=0.7,
-                          )
-        
+        llm = ChatOpenAI(model="gpt-4o-mini",
+                    openai_api_key=self.OPENAI_API_KEY, 
+                    max_tokens = 512,
+                    temperature=0.7,
+                    )
+        return llm
+
     def get_response(self, text) -> list:
-
-        tmp_prompt = f"{self.template}\n Now at {datetime.now()}, please break down this text: {text}"
-        response = self.llm(tmp_prompt)
-        # print('raw', response)
-        response = self.get_tasks_from_string(response)
+        prompt = PromptTemplate.from_template(
+            "{template}\nNow at {current_time}, please break down this text: {input_text}\n{format_instructions}"
+        )
+
+        formatted_prompt = prompt.format(
+            template=self.template,
+            current_time=datetime.now(),
+            input_text=text,
+            format_instructions=self.output_parser.get_format_instructions()
+        )
+
+        response = self.llm.invoke(formatted_prompt)
+        response = self.get_tasks_from_string(response.content)
         return response
 
     def get_tasks_from_string(self, text:str) -> list:
         # Find the substring between '[' and ']'
         start_index = text.find('[')
         end_index = text.find(']') + 1
         json_string = text[start_index:end_index]
+        json_string = json_string.replace('true', 'True').replace('false', 'False').replace('null', 'None')
+
         tasks_list = eval(json_string)
         return tasks_list
 
@@ -439,7 +454,7 @@ def segment_text(self, result) -> str:
         segmented_text = self._post_processing_transcribed_text(segmented_text)
 
         try:
-            # Send the joined text to self.task_seperator.get_response() to get a list
+        # Send the joined text to self.task_seperator.get_response() to get a list
             self.response_list = self.task_seperator.get_response(text=segmented_text)
             # Separate the received list by newline
             titles = [task['title'] for task in self.response_list]
@@ -477,11 +492,10 @@ def get_task_list(self):
 
 
 if __name__ == "__main__":
-
     audio_path='data/audio_2.ogg'
     speech_to_text = SpeechSummaryProcessor(audio_path=audio_path)
     text = speech_to_text.generate_speech_to_text()
-    print ('Text', text)
+    print ('Text:', text)
 
     # symbol = 'SSI'
     # date_format='year'