Update utils.py

utils.py

@@ -1,119 +1,119 @@
-import json
-import time
-from transformers import AutoTokenizer, AutoModel
-from langchain_community.chat_models import ChatOpenAI
-import pandas as pd
-from config import settings
-from langchain_core.utils.function_calling import convert_to_openai_function
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.agents.format_scratchpad import format_to_openai_functions
-from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.agents import AgentExecutor
-
-from tools import MEMORY, set_chatbot_name, close_chat, recommand_podcast,app_features,joke_teller,SESSION_ID
-
-from database_functions import get_chat_bot_name,get_chat_history, get_last_conversion, get_last_session, get_mood_data
-
-
-def get_mood_summary(user_id):
-
-    data = get_mood_data(user_id)
-    system_prompt = """You are an descripting assistant that provides the breif description of the user data which is related to their mood tracking activity. Properly descibe the reason for their mood.Avoid times and dates in description
-                     Here is the user data: {data}"""
-
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL,
-                     openai_api_key=settings.OPENAI_KEY, temperature=0.0)
-    
-    return llm.invoke(system_prompt.format(data=data)).content
-
-
-def deanonymizer(input, anonymizer):
-    input = anonymizer.deanonymize(input)
-    map = anonymizer.deanonymizer_mapping
-    if map:
-        for k in map["PERSON"]:
-            names = k.split(" ")
-            for i in names:
-                input = input.replace(i, map["PERSON"][k])
-    return input
-
-
-
-def get_last_session_summary(last_session_id, second_last_session_id):
-
-    conversation = get_last_conversion(last_session_id,second_last_session_id) 
-    if conversation:
-        system_prompt = """ context: there is one typical conversation going on between two high school gen z girls.
-        you are one of the high school gen z girl. your voice is edgy and raw. 
-        must use I for AI named BMOXI and for human use my friend. in summary.
-        this is your conversation with your best friend. summerize whole conversation and if you found any question can be asked from chat then return summary and qustion else return summary and append None at last.
-        conversation: {conversation}
-        summary: 
-        """
-
-        llm = ChatOpenAI(model=settings.OPENAI_MODEL,
-                        openai_api_key=settings.OPENAI_KEY, temperature=0.0)
-
-        response = llm.invoke(system_prompt.format(conversation=conversation)).content
-        return response
-    else:
-        return ""
-
-def create_agent(user_id,is_first = False):
-    # print("get user Id**********************",user_id)
-    
-    previous_session_id = get_last_session(user_id)
-    # print(previous_session_id)
-   
-    tools = [set_chatbot_name,close_chat,recommand_podcast,app_features,joke_teller]
-
-    functions = [convert_to_openai_function(f) for f in tools]
-    model = ChatOpenAI(model_name=settings.OPENAI_MODEL,
-                       openai_api_key=settings.OPENAI_KEY, frequency_penalty= 1, temperature=0.7).bind(functions=functions)
- 
-    chat_bot_name = get_chat_bot_name(user_id)
-
-    extra_prompt = ""
-    previous_problem_summary = None
-    if is_first:
-        start = time.time()
-        mood_summary = get_mood_summary(user_id)
-        print(previous_session_id)
-        if previous_session_id['second_last_session_id']:
-            previous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id'])
-
-        print("time require for mood summary: ",time.time()-start)
-        if previous_problem_summary.find('None') == -1:
-            extra_prompt = f"""ask user her previous problem is solved or not.use previous problem summary for framming the question. must include her name which is {user_id} .nothing else."""
-        else:
-            extra_prompt = f""" Only use these templates to start conversation:-
-                                1. Hey again {user_id}! How's it going? 
-                                2. hey again {user_id}! What's up today? Need  ✨ Advice, ✨ a Mood Boost, ✨ a Chat, ✨ Resource Suggestions, ✨ App Features help?  How can I help?"
-                                use any one of the question for response based on your understanding not use anything else simply return one of these two only.
-                            """
-                            
-                            
-    prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood="", previous_summary=previous_problem_summary)+extra_prompt),
-                                               MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
-                                               MessagesPlaceholder(variable_name="agent_scratchpad")])
-
-
-
-    memory = ConversationBufferWindowMemory(memory_key="chat_history", chat_memory=get_chat_history(
-        previous_session_id['last_session_id']), return_messages=True, k=5)
-    
-    # print("memory created")
-    global MEMORY,SESSION_ID
-    MEMORY = memory
-    SESSION_ID = previous_session_id['last_session_id']
-
-    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | model | OpenAIFunctionsAgentOutputParser()
-
-    agent_executor = AgentExecutor(
-        agent=chain, tools=tools, memory=memory, verbose=True)
-
-    return agent_executor
-
-
+import json
+import time
+from transformers import AutoTokenizer, AutoModel
+from langchain_community.chat_models import ChatOpenAI
+import pandas as pd
+from config import settings
+from langchain_core.utils.function_calling import convert_to_openai_function
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.agents import AgentExecutor
+
+from tools import MEMORY, set_chatbot_name, close_chat, recommand_podcast,app_features,joke_teller,SESSION_ID
+
+from database_functions import get_chat_bot_name,get_chat_history, get_last_conversion, get_last_session, get_mood_data
+
+
+def get_mood_summary(user_id):
+
+    data = get_mood_data(user_id)
+    system_prompt = """You are an descripting assistant that provides the breif description of the user data which is related to their mood tracking activity. Properly descibe the reason for their mood.Avoid times and dates in description
+                     Here is the user data: {data}"""
+
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL,
+                     openai_api_key=settings.OPENAI_KEY, temperature=0.0)
+    
+    return llm.invoke(system_prompt.format(data=data)).content
+
+
+def deanonymizer(input, anonymizer):
+    input = anonymizer.deanonymize(input)
+    map = anonymizer.deanonymizer_mapping
+    if map:
+        for k in map["PERSON"]:
+            names = k.split(" ")
+            for i in names:
+                input = input.replace(i, map["PERSON"][k])
+    return input
+
+
+
+def get_last_session_summary(last_session_id, second_last_session_id):
+
+    conversation = get_last_conversion(last_session_id,second_last_session_id) 
+    if conversation:
+        system_prompt = """ context: there is one typical conversation going on between two high school gen z girls.
+        you are one of the high school gen z girl. your voice is edgy and raw. 
+        must use I for AI named BMOXI and for human use my friend. in summary.
+        this is your conversation with your best friend. summerize whole conversation and if you found any question can be asked from chat then return summary and qustion else return summary and append None at last.
+        conversation: {conversation}
+        summary: 
+        """
+
+        llm = ChatOpenAI(model=settings.OPENAI_MODEL,
+                        openai_api_key=settings.OPENAI_KEY, temperature=0.0)
+
+        response = llm.invoke(system_prompt.format(conversation=conversation)).content
+        return response
+    else:
+        return ""
+
+def create_agent(user_id,is_first = False):
+    # print("get user Id**********************",user_id)
+    
+    previous_session_id = get_last_session(user_id)
+    # print(previous_session_id)
+   
+    tools = [set_chatbot_name,close_chat,recommand_podcast,app_features,joke_teller]
+
+    functions = [convert_to_openai_function(f) for f in tools]
+    model = ChatOpenAI(model_name=settings.OPENAI_MODEL,
+                       openai_api_key=settings.OPENAI_KEY, frequency_penalty= 1, temperature=0.7).bind(functions=functions)
+ 
+    chat_bot_name = get_chat_bot_name(user_id)
+
+    extra_prompt = ""
+    previous_problem_summary = None
+    if is_first:
+        start = time.time()
+        mood_summary = get_mood_summary(user_id)
+        print(previous_session_id)
+        if previous_session_id['second_last_session_id']:
+            previous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id'])
+
+        print("time require for mood summary: ",time.time()-start)
+        if previous_problem_summary.find('None') == -1:
+            extra_prompt = f"""ask user her previous problem is solved or not.use previous problem summary for framming the question. must include her name which is {user_id} .nothing else."""
+        else:
+            extra_prompt = f""" Only use these templates to start conversation:-
+                                1. Hey again {user_id}! How's it going? 
+                                2. hey again {user_id}! What's up today? Need  ✨ Advice, ✨ a Mood Boost, ✨ a Chat, ✨ Resource Suggestions, ✨ App Features help?  How can I help?"
+                                use any one of the question for response based on your understanding not use anything else simply return one of these two only. also must include her name which is {userid}. nothing else.
+                            """
+                            
+                            
+    prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood="", previous_summary=previous_problem_summary)+extra_prompt),
+                                               MessagesPlaceholder(variable_name="chat_history"), ("user", "{input}"),
+                                               MessagesPlaceholder(variable_name="agent_scratchpad")])
+
+
+
+    memory = ConversationBufferWindowMemory(memory_key="chat_history", chat_memory=get_chat_history(
+        previous_session_id['last_session_id']), return_messages=True, k=5)
+    
+    # print("memory created")
+    global MEMORY,SESSION_ID
+    MEMORY = memory
+    SESSION_ID = previous_session_id['last_session_id']
+
+    chain = RunnablePassthrough.assign(agent_scratchpad=lambda x: format_to_openai_functions(x["intermediate_steps"])) | prompt | model | OpenAIFunctionsAgentOutputParser()
+
+    agent_executor = AgentExecutor(
+        agent=chain, tools=tools, memory=memory, verbose=True)
+
+    return agent_executor
+
+