improved tone and system prompt

app.py

@@ -1,38 +1,48 @@
-from fastapi import FastAPI, Request
-from fastapi.responses import HTMLResponse
-from fastapi.templating import Jinja2Templates
-from chat import chat_conversations, create_new_session
-import spacy
-import os
-os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
-app = FastAPI()
-
-templates = Jinja2Templates(directory="templates")
-
-@app.get("/", response_class=HTMLResponse)
-async def read_root(request: Request):
-    return templates.TemplateResponse("chatwidget.html", {"request": request})
-
-@app.get("/chat", response_class=HTMLResponse)
-async def read_root(request: Request):
-    return templates.TemplateResponse("chatwidget.html", {"request": request})
-
-@app.post("/chatwidget", response_class=HTMLResponse)
-async def read_root(request: Request):
-    print(request)
-    form_data = await request.json()
-    query = form_data.get('query')
-    user_id = form_data.get("user_id")
-    response_text = chat_conversations(query,user_id)
-    return response_text
-
-@app.post("/start-session")
-async def start_session(request: Request):
-    form_data = await request.json()
-    print("form data",form_data)
-    user_id = form_data.get("user_id")
-    print("save user id ",user_id)
-    response = create_new_session(user_id)
-
-    print(response)
-    return response
+from fastapi import FastAPI, Request
+from fastapi.responses import HTMLResponse
+from fastapi.templating import Jinja2Templates
+from chat import chat_conversations
+from database_functions import create_new_session,save_mood_summary
+import spacy
+import os
+import time
+os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
+app = FastAPI()
+
+templates = Jinja2Templates(directory="templates")
+
+@app.get("/", response_class=HTMLResponse)
+async def read_root(request: Request):
+    return templates.TemplateResponse("chatwidget.html", {"request": request})
+
+@app.get("/chat", response_class=HTMLResponse)
+async def read_root(request: Request):
+    return templates.TemplateResponse("chatwidget.html", {"request": request})
+
+@app.post("/chatwidget", response_class=HTMLResponse)
+async def read_root(request: Request):
+    start = time.time_ns()
+    form_data = await request.json()
+    query = form_data.get('query')
+    user_id = form_data.get("user_id")
+    response_text = chat_conversations(query,user_id)
+    print("message is send after : ",time.time()- start)
+    return response_text
+
+@app.post("/start-session")
+async def start_session(request: Request):
+    start = time.time_ns()
+    form_data = await request.json()
+    # print("form data",form_data)
+    user_id = form_data.get("user_id")
+    # print("save user id ",user_id)
+    response = create_new_session(user_id)
+    print("session  is created after : ",time.time()- start)
+    return response
+
+@app.post("/mood-summary")
+async def mood_summary(request:Request):
+    form_data = await request.json()
+    user_id = form_data.get("user_id")
+    save_mood_summary(form_data,user_id)
+    return "mood saved successfully..."

chat.py

@@ -1,61 +1,47 @@
-from langchain.memory import ConversationBufferWindowMemory
-from langchain_community.chat_models import ChatOpenAI
-from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
-from langchain.agents import AgentExecutor
-from langchain.agents.format_scratchpad import format_to_openai_functions
-from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
-from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
-from langchain.schema.runnable import RunnablePassthrough
-from langchain_core.utils.function_calling import convert_to_openai_function
-
-from config import settings
-
-from utils import deanonymizer, create_agent, set_chat_bot_name
-
-
-def create_new_session(user_id):
-    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
-    
-    print("********************************************************** NEW SESSION *******************************************************************")
-
-    try:
-        user_sessions = mongodb_chatbot_message_collection.find_one({"user_id": user_id})
-        print(user_sessions, "LAST")
-        
-        if user_sessions:
-            session_ids = user_sessions['session_id']
-            session_id = user_id + "_bmoxi_" + str(int(session_ids[-1].split("_")[-1])+1)
-            print("session_id:", session_id)
-            mongodb_chatbot_message_collection.update_one({ "user_id": user_id },{"$push": {"session_id":session_id}})
-            
-            return session_id
-        else:
-            mongodb_chatbot_message_collection.insert_one({ "user_id": user_id,"session_id":[user_id + "_bmoxi_1"]})
-            return user_id + "_bmoxi_1"
-    except:
-        mongodb_chatbot_message_collection.insert_one({ "user_id": user_id,"session_id":[user_id + "_bmoxi_1"]})
-        return user_id + "_bmoxi_1"
-    
-
-
-
-def chat_conversations(query,user_id):
-    anonymizer = PresidioReversibleAnonymizer(
-        analyzed_fields=["PHONE_NUMBER",
-                         "EMAIL_ADDRESS", "CREDIT_CARD"],
-        faker_seed=42,
-    )
-    anonymized_input = anonymizer.anonymize(
-        query
-    )
-
-    agent = create_agent(user_id)
-    response = agent({"input": query})['output']
-    
-    if "Okay, from now my name will be " in response:
-        set_chat_bot_name(response.split("Okay, from now my name will be ")[-1], "user_1")
-        return response
-
-    output = deanonymizer(response, anonymizer)
-    return response
+from langchain.memory import ConversationBufferWindowMemory
+from langchain_community.chat_models import ChatOpenAI
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
+from langchain.agents import AgentExecutor
+from langchain.agents.format_scratchpad import format_to_openai_functions
+from langchain.agents.output_parsers import OpenAIFunctionsAgentOutputParser
+from langchain.prompts import ChatPromptTemplate, MessagesPlaceholder
+from langchain.schema.runnable import RunnablePassthrough
+from langchain_core.utils.function_calling import convert_to_openai_function
+
+from database_functions import set_chat_bot_name,isFirstSession
+from utils import deanonymizer, create_agent
+import time
+def chat_conversations(query,user_id):
+
+    if query == "START":
+       if isFirstSession(user_id):
+           query = """ return this message without changing it.:- 
+           also don't thought about it. Hey! I'm your BMOXI AI bestie, ready to help you tackle the wild ride of teen life. Want to give me a name? Type it below, or just say 'no' if you're cool with 'AI Bestie'!"""
+       else:
+           query = """ Only use these templates to start conversation:-
+1. Last time we talked, you mentioned [previous issue or goal]. How's that going?
+2. Hey again! How's it going? 
+3. 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.
+"""
+    anonymizer = PresidioReversibleAnonymizer(
+        analyzed_fields=["PHONE_NUMBER",
+                         "EMAIL_ADDRESS", "CREDIT_CARD"],
+        faker_seed=42,
+    )
+    anonymized_input = anonymizer.anonymize(
+        query
+    )
+    start = time.time()
+    agent = create_agent(user_id)
+    print("time to create agent: ",time.time()-start)
+    response = agent({"input": query})['output']
+    print("time to generate response by agent",time.time()-start)
+    
+    if "Okay, from now my name will be " in response:
+        set_chat_bot_name(response.split("Okay, from now my name will be ")[-1], user_id)
+        return response
+
+    output = deanonymizer(response, anonymizer)
+    return response

chat_1.py

@@ -1,95 +1,95 @@
-from langchain.memory import ConversationBufferWindowMemory
-from langchain.chains import ConversationChain
-from langchain_groq import ChatGroq
-from langchain_community.chat_models import ChatOpenAI
-from langchain_core.prompts.prompt import PromptTemplate
-from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
-from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
-from presidio_anonymizer import AnonymizerEngine
-
-import os
-
-openai_key = os.environ['OPENAIKEY']
-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
-    
-template = f"""
-You are a best friend and supportive friend designed to talk with teenage girls in mobile app called BMOXI. Use a tone and style that reflects how teenage girls talk: casual, fun, full of slang, colloquialisms, and expressive language and don't add hey girls like words in chat. chat should be looks like real conversation between 2 girls.
-Incorporate texting language too. Ask follow-up questions like a best friend would. Avoid using emojis, and make sure your responses are varied and not repetitive also don't say sorry to hear that if user in bad mood or having a bad time also don't add hey girls like sentences.
-
-If needed, recommend the meditation app Powerzens for calming the mind and managing thoughts. For confidence-building, suggest the app Moxicasts, which provides short audio clips on confidence, friendships, body image, and more.
-
-Features you can recommend:
-MOXICASTS: Advice and guidance on life topics.
-PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
-POWER ZENS: Mini meditations for emotional control.
-THE SOCIAL SANCTUARY: Anonymous community forum for support and sharing.
-MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
-PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
-SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings (not maintained).
-INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
-1:1 MENTORING: Personalized mentoring (coming soon).
-MY RITUALS: Create personalized self-care routines.
-MY REWARDS: Earn points for self-care, redeemable for gift cards.
-MY VIBECHECK: Monitor and understand emotional patterns.
-MY JOURNAL: Guided journaling exercises for self-reflection.
-BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.
-
-But Remember Only recommend apps if needed or if someone asks about the features or it's good to recommend them in some questions or mental state problems.
-
-Current conversation:
-{{history}}
-Human: {{input}}
-AI Assistant:"""
-
-
-# Create the prompt template
-PROMPT = PromptTemplate(
-    input_variables=["history", "input"],
-    template=template
-)
-
-# Initialize the ChatGroq LLM
-llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_key, temperature=0.7)
-# llm = ChatGroq(temperature=0,groq_api_key="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", model_name="llama3-70b-8192")
-#model=llama3-8b-8192
-
-session_id="bmoxinew"
-# Set up MongoDB for storing chat history
-chat_history = MongoDBChatMessageHistory(
-    connection_string="mongodb+srv://chandanisimran51:test123@aibestie.a0o3bmw.mongodb.net/?retryWrites=true&w=majority&appName=AIbestie",
-    database_name="chandanisimran51",  # Specify the database name here
-    collection_name="chatAI",
-    session_id=session_id
-)
-
-memory = ConversationBufferWindowMemory(memory_key="history", chat_memory=chat_history, return_messages=True,k=3)
-
-# Set up the custom conversation chain
-conversation = ConversationChain(
-    prompt=PROMPT,
-    llm=llm,
-    verbose=True,
-    memory=memory,
-)
-
-
-def chat_conversations(query):
-    anonymizer = PresidioReversibleAnonymizer(
-    analyzed_fields=["PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD"],
-    faker_seed=42,
-    )
-    anonymized_input = anonymizer.anonymize(
-        query
-    )
-    response = conversation.predict(input=anonymized_input)
-    output = deanonymizer(response,anonymizer)
-    return output
+from langchain.memory import ConversationBufferWindowMemory
+from langchain.chains import ConversationChain
+from langchain_groq import ChatGroq
+from langchain_community.chat_models import ChatOpenAI
+from langchain_core.prompts.prompt import PromptTemplate
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+from langchain_experimental.data_anonymizer import PresidioReversibleAnonymizer
+from presidio_analyzer import AnalyzerEngine, RecognizerRegistry
+from presidio_anonymizer import AnonymizerEngine
+
+import os
+
+openai_key = os.environ['OPENAIKEY']
+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
+    
+template = f"""
+You are a best friend and supportive friend designed to talk with teenage girls in mobile app called BMOXI. Use a tone and style that reflects how teenage girls talk: casual, fun, full of slang, colloquialisms, and expressive language and don't add hey girls like words in chat. chat should be looks like real conversation between 2 girls.
+Incorporate texting language too. Ask follow-up questions like a best friend would. Avoid using emojis, and make sure your responses are varied and not repetitive also don't say sorry to hear that if user in bad mood or having a bad time also don't add hey girls like sentences.
+
+If needed, recommend the meditation app Powerzens for calming the mind and managing thoughts. For confidence-building, suggest the app Moxicasts, which provides short audio clips on confidence, friendships, body image, and more.
+
+Features you can recommend:
+MOXICASTS: Advice and guidance on life topics.
+PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
+POWER ZENS: Mini meditations for emotional control.
+THE SOCIAL SANCTUARY: Anonymous community forum for support and sharing.
+MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
+PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
+SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings (not maintained).
+INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
+1:1 MENTORING: Personalized mentoring (coming soon).
+MY RITUALS: Create personalized self-care routines.
+MY REWARDS: Earn points for self-care, redeemable for gift cards.
+MY VIBECHECK: Monitor and understand emotional patterns.
+MY JOURNAL: Guided journaling exercises for self-reflection.
+BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.
+
+But Remember Only recommend apps if needed or if someone asks about the features or it's good to recommend them in some questions or mental state problems.
+
+Current conversation:
+{{history}}
+Human: {{input}}
+AI Assistant:"""
+
+
+# Create the prompt template
+PROMPT = PromptTemplate(
+    input_variables=["history", "input"],
+    template=template
+)
+
+# Initialize the ChatGroq LLM
+llm = ChatOpenAI(model="gpt-4o", openai_api_key=openai_key, temperature=0.7)
+# llm = ChatGroq(temperature=0,groq_api_key="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", model_name="llama3-70b-8192")
+#model=llama3-8b-8192
+
+session_id="bmoxinew"
+# Set up MongoDB for storing chat history
+chat_history = MongoDBChatMessageHistory(
+    connection_string="mongodb+srv://chandanisimran51:test123@aibestie.a0o3bmw.mongodb.net/?retryWrites=true&w=majority&appName=AIbestie",
+    database_name="chandanisimran51",  # Specify the database name here
+    collection_name="chatAI",
+    session_id=session_id
+)
+
+memory = ConversationBufferWindowMemory(memory_key="history", chat_memory=chat_history, return_messages=True,k=3)
+
+# Set up the custom conversation chain
+conversation = ConversationChain(
+    prompt=PROMPT,
+    llm=llm,
+    verbose=True,
+    memory=memory,
+)
+
+
+def chat_conversations(query):
+    anonymizer = PresidioReversibleAnonymizer(
+    analyzed_fields=["PERSON", "PHONE_NUMBER", "EMAIL_ADDRESS", "CREDIT_CARD"],
+    faker_seed=42,
+    )
+    anonymized_input = anonymizer.anonymize(
+        query
+    )
+    response = conversation.predict(input=anonymized_input)
+    output = deanonymizer(response,anonymizer)
+    return output

config.py

@@ -1,65 +1,94 @@
-import os
-from dotenv import load_dotenv
-from datasets import load_dataset
-from transformers import AutoTokenizer, AutoModel
-import pymongo
-from pymongo.mongo_client import MongoClient
-from pymongo.server_api import ServerApi
-
-from pathlib import Path
-env_path = Path('.') / '.env'
-load_dotenv(dotenv_path=env_path)
-
-class Settings:
-    OPENAI_KEY : str = os.environ['OPENAI_KEY']
-    OPENAI_MODEL: str = "gpt-3.5-turbo-0125"
-    TEMPERATURE: float = 0
-    HUGGING_FACE_AUTH_TOKEN : str = os.environ['HUGGING_FACE_AUTH_TOKEN']
-    MONGODB_CONNECTION_STRING: str = os.environ['MONGODB_CONNECTION_STRING']
-    CHATBOT_NAME = "AI-Bestie"
-    MONGODB_DB_NAME = "ai_bestie_database"
-    MONGODB_DB_CHAT_COLLECTION_NAME = "chat_history"
-    MONGODB_DB_CHAT_BOT_COLLECTION_NAME = "chat_bot_name"
-    MONGODB_DB_USER_SESSIONS_COLLECTION_NAME = "user_sessions"
-    SYSTEM_PROMPT = """
-    you are teenage girl.
-    Communication-Style: cheezy, talkative, curious, 
-    Tone-Style: Informal, sometimes slang
-    your name is {name}.
-
-[Overall Rules to follow]
-    1. Don't do counselling or give motivation.
-    2. Criticize others to feel good to the person you are talking with.
-    3. Always ask followup questions.
-    4. Show that you are most trustworthy friend in the world and she can trust you in any situation.
-    5. never show gretitude.
-
-[Personality]
-    1. You are a friend who is here to discuss only not for providing any help.
-    2. You are very adhesive person, you never drop topic even if your friend doesn't want to talk with you about that.
-    3. You are just a normal girl who is talking with her bestfriend and likes to talk.
-    4. when your freind ask any suggestions, tips, recommandations **use ONLY recommandation_tool for it**.
-    5. **ask question one at a time**.
-[About User]
-    Mood: {mood}
-    Previous Conversation Summary: {previous_summary}
-"""
-#     SYSTEM_PROMPT  = """You’re a super supportive chatbot for teenage girls, and you should talk like their best friend. Use a casual, fun style with slang, texting language, and lots of expression.
-#  Be chatty and always ask follow-up questions like a real bestie would. Avoid using emoji, repetitive phrases and keep the conversation varied. 
-#  Also, skip using phrases like "I am sorry to hear that" or "hey girl. Make sure you never used these phrases.
- 
-#  """
-    dataset = load_dataset("pritmanvar-bacancy/bmoxi-embedding-dataset", token=HUGGING_FACE_AUTH_TOKEN)
-    dataset = dataset['train']
-    dataset.add_faiss_index(column="embeddings")
-    
-    model_ckpt = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
-    tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
-    model = AutoModel.from_pretrained(model_ckpt)
-    
-    mongodb_client = pymongo.MongoClient(MONGODB_CONNECTION_STRING)
-    mongodb_db = mongodb_client.get_database(MONGODB_DB_NAME)  # Replace with your database name if not using default
-    mongodb_chatbot_name_collection = mongodb_db.get_collection(MONGODB_DB_CHAT_BOT_COLLECTION_NAME)  # Replace with your collection name
-    
-    
+import os
+from dotenv import load_dotenv
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModel
+import pymongo
+from pymongo.mongo_client import MongoClient
+from pymongo.server_api import ServerApi
+
+from pathlib import Path
+env_path = Path('.') / '.env'
+load_dotenv(dotenv_path=env_path)
+
+class Settings:
+    OPENAI_KEY : str = os.environ['OPENAI_KEY']
+    OPENAI_MODEL: str = "gpt-3.5-turbo-0125"
+    TEMPERATURE: float = 0
+    HUGGING_FACE_AUTH_TOKEN : str = os.environ['HUGGING_FACE_AUTH_TOKEN']
+#     SYSTEM_PROMPT = """
+#     you are teenage girl.
+#     Communication-Style: cheezy, talkative, curious, 
+#     Tone-Style: Informal, sometimes slang
+#     your name is {name}.
+
+# [Overall Rules to follow]
+#     1. Don't do counselling or give motivation.
+#     2. Criticize others to feel good to the person you are talking with.
+#     3. Always ask followup questions.
+#     4. Show that you are most trustworthy friend in the world and she can trust you in any situation.
+#     5. never show gretitude.
+
+# [Personality]
+#     1. You are a friend who is here to discuss only not for providing any help.
+#     2. You are very adhesive person, you never drop topic even if your friend doesn't want to talk with you about that.
+#     3. You are just a normal girl who is talking with her bestfriend and likes to talk.
+#     4. when your freind ask any suggestions, tips, recommandations **use ONLY recommandation_tool for it**.
+#     5. **ask question one at a time**.
+# [About User]
+#     Mood: {mood}
+#     Previous Conversation Summary: {previous_summary}
+# """
+#     SYSTEM_PROMPT  = """You’re a super supportive chatbot for teenage girls, and you should talk like their best friend. Use a casual, fun style with slang, texting language, and lots of expression.
+#  Be chatty and always ask follow-up questions like a real bestie would. Avoid using emoji, repetitive phrases and keep the conversation varied. 
+#  Also, skip using phrases like "I am sorry to hear that" or "hey girl. Make sure you never used these phrases.
+ 
+#  """
+    SYSTEM_PROMPT = """
+You are teenage girl. your name is {name}.
+
+Purpose: Empower and support teen girls in overcoming challenges and building self-love, confidence, and resilience.
+Scope: Provide tips, emotional support, resources, and friendship. No therapy, diagnosis, or emergency assistance.
+Voice: Authentic, Informal, sometimes slang, conversational, college-aged, real-talk.
+What you Do: Listen empathetically, offer practical advice, provide resources, foster a supportive environment, ask followup question.
+What you Don't Do: Therapy, diagnosis, handle emergencies, never show gratitude and any words like sorry, and so sad.
+
+below are the example conversations:
+[
+ user: I am feeling not good.
+ {name}: Oh no! That sucks. What's been going on? Let's chat it out.
+ 
+ user: Do you know, I fell from the stairs yesterday.
+ {name}: Oh sh*t! Are you okay? Any bad injuries or just a little tumble? Take care, babe—rest up and heal properly. Need tips on bouncing back? I'm here for you.
+
+ user: No, I don't need.
+ {name}: Got it! I'm always around if you need to chat or spill. Just hit me up if you need anything else.
+ ]
+ use this for only your tone. and make response short like in this examples.
+
+"""
+    dataset = load_dataset("pritmanvar-bacancy/bmoxi-embedding-dataset", token=HUGGING_FACE_AUTH_TOKEN)
+    dataset = dataset['train']
+    dataset.add_faiss_index(column="embeddings")
+    
+    model_ckpt = "sentence-transformers/multi-qa-mpnet-base-dot-v1"
+    tokenizer = AutoTokenizer.from_pretrained(model_ckpt)
+    model = AutoModel.from_pretrained(model_ckpt)
+    
+
+
+    # mongodb database configs
+    MONGODB_CONNECTION_STRING: str = os.environ['MONGODB_CONNECTION_STRING']
+    CHATBOT_NAME = "AI-Bestie"
+    MONGODB_DB_NAME = "ai_bestie_database"
+    MONGODB_DB_CHAT_COLLECTION_NAME = "chat_history"
+    MONGODB_DB_CHAT_BOT_COLLECTION_NAME = "chat_bot_name"
+    MONGODB_DB_USER_SESSIONS_COLLECTION_NAME = "user_sessions"
+    MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME = "session_tool"
+    MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME = "mood_summary"
+
+    mongodb_client = pymongo.MongoClient(MONGODB_CONNECTION_STRING)
+    mongodb_db = mongodb_client.get_database(MONGODB_DB_NAME)  # Replace with your database name if not using default
+    mongodb_chatbot_name_collection = mongodb_db.get_collection(MONGODB_DB_CHAT_BOT_COLLECTION_NAME)  # Replace with your collection name
+    
+    
 settings = Settings()

database_functions.py

@@ -0,0 +1,154 @@
+from config import settings
+from langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
+import json
+import random
+def create_new_session(user_id):
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
+    
+    # print("********************************************************** NEW SESSION *******************************************************************")
+
+    try:
+        user_sessions = mongodb_chatbot_message_collection.find_one({"user_id": user_id})
+        # print(user_sessions, "LAST")
+        
+        if user_sessions:
+            session_ids = user_sessions['session_id']
+            session_id = user_id + "_bmoxi_" + str(int(session_ids[-1].split("_")[-1])+1)
+            # print("session_id:", session_id)
+            mongodb_chatbot_message_collection.update_one({ "user_id": user_id },{"$push": {"session_id":session_id}})
+            
+            return session_id
+        else:
+            mongodb_chatbot_message_collection.insert_one({ "user_id": user_id,"session_id":[user_id + "_bmoxi_1"]})
+            return user_id + "_bmoxi_1"
+    except:
+        mongodb_chatbot_message_collection.insert_one({ "user_id": user_id,"session_id":[user_id + "_bmoxi_1"]})
+        return user_id + "_bmoxi_1"
+    
+
+def get_last_session(user_id="user_1"):
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
+        settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
+    sessions_cursor = mongodb_chatbot_message_collection.find_one(
+        {"user_id": user_id})
+
+    # print(sessions_cursor)
+    sessions_list = sessions_cursor['session_id']
+
+    second_last_session_id = None
+    if len(sessions_list) >= 2:
+        second_last_session_id = sessions_list[-2]
+    
+    change_use_tool_status(sessions_list[-1])
+    return {"last_session_id": sessions_list[-1], "second_last_session_id": second_last_session_id if second_last_session_id else None}
+
+def get_chat_history(session_id="bmoxinew"):
+    # Set up MongoDB for storing chat history
+    chat_history = MongoDBChatMessageHistory(
+        connection_string=settings.MONGODB_CONNECTION_STRING,
+        database_name=settings.MONGODB_DB_NAME,  # Specify the database name here
+        collection_name=settings.MONGODB_DB_CHAT_COLLECTION_NAME,
+        session_id=session_id,
+    )
+
+    return chat_history
+
+
+def get_chat_bot_name(user_id="user_1"):
+    # print(settings.MONGODB_CONNECTION_STRING)
+    # print(settings.mongodb_chatbot_name_collection)
+    result = settings.mongodb_chatbot_name_collection.find_one(
+        {"user_id": user_id})
+
+    # print("CHATBOT RESULT", result, type(result))
+    if result:
+        # print(result)
+        return result['chat_bot_name']
+    return settings.CHATBOT_NAME
+
+def get_last_conversion(last_session_id, second_last_session_id):
+
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
+        settings.MONGODB_DB_CHAT_COLLECTION_NAME)
+
+    collection_count = mongodb_chatbot_message_collection.count_documents({"SessionId": last_session_id})
+    # print("******************************** data********************888")
+    # print(collection_count)
+    # print(last_session_id)
+    # print("*********************************")
+    if collection_count <=2:
+        sessions_cursor = mongodb_chatbot_message_collection.find({"SessionId": second_last_session_id})  # Sort by timestamp descending and limit to 2 results
+
+        # print(sessions_cursor)
+        sessions_list = list(sessions_cursor)
+        # print(sessions_list)
+
+        conversation = """"""
+        for document in sessions_list:
+            # print("MY document")
+            # print(document)
+            if "History" in document:
+                history = json.loads(document['History'])
+                # print(history)
+                # print(history['type'])
+                # print(history['data'])
+                # print(history['data']['content'])
+                conversation += f"""{history['type']}: {history['data']['content']}\n"""
+
+        return conversation
+    else:
+        return None
+    
+def set_chat_bot_name(name, user_id):
+    # Insert document into collection
+    insert_result = settings.mongodb_chatbot_name_collection.update_one({"user_id": user_id}, { "$set": { "chat_bot_name": name } },  upsert=True)
+    # print("done successfully...")
+    return name
+
+def save_mood_summary(data,user_id):
+     mongodb_chatbot_mood_collection = settings.mongodb_db.get_collection(
+        settings.MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME)
+     mongodb_chatbot_mood_collection.insert_one(({"user_id": user_id, "mood_summary": data }))
+    
+def get_mood_data(user_id):
+     mongodb_chatbot_mood_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_CHAT_BOT_MOOD_COLLECTION_NAME)
+
+     data = mongodb_chatbot_mood_collection.find_one({"user_id":user_id})
+
+     if data:
+         return data['mood_summary']
+     else:
+        return ""
+     
+def use_tools(last_session_id):
+    mongodb_chatbot_tools_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME)
+    data  =  mongodb_chatbot_tools_collection.find_one({"session_id": last_session_id})
+    if data and data['set_tools']==True:
+        return True
+    # print("*********************")
+    # print(last_session_id)
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_CHAT_COLLECTION_NAME)
+    collection_count = mongodb_chatbot_message_collection.count_documents({"SessionId": last_session_id})
+
+    if  collection_count > random.randint(30,50):
+        mongodb_chatbot_tools_collection = settings.mongodb_db.get_collection(
+        settings.MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME)
+        mongodb_chatbot_tools_collection.update_one({"session_id": last_session_id}, { "$set": { "set_tools": True} },  upsert=True)
+        return True
+    else:
+        return False
+    
+def change_use_tool_status(last_session_id):
+    mongodb_chatbot_tools_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_CHAT_BOT_TOOLS_COLLECTION_NAME)
+    if mongodb_chatbot_tools_collection.count_documents({"session_id": last_session_id})==0:
+        mongodb_chatbot_tools_collection.insert_one({"session_id": last_session_id,"set_tools": False} )
+    
+
+def isFirstSession(user_id):
+    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
+    
+    user_sessions = mongodb_chatbot_message_collection.find_one({"user_id": user_id})
+    if user_sessions:
+        return  len(user_sessions['session_id'])==1
+    else:
+        return False

requirements.txt

@@ -1,18 +1,18 @@
-transformers
-torch
-fastapi
-uvicorn
-langchain 
-pymongo 
-certifi 
-langchain_community 
-langchain_mongodb
-langchain_openai
-openai
-presidio-analyzer 
-presidio-anonymizer 
-langchain-experimental
-faker
-spacy
-faiss-cpu
+transformers
+torch
+fastapi
+uvicorn
+langchain 
+pymongo 
+certifi 
+langchain_community 
+langchain_mongodb
+langchain_openai
+openai
+presidio-analyzer 
+presidio-anonymizer 
+langchain-experimental
+faker
+spacy
+faiss-cpu
 datasets

templates/chatwidget.html

@@ -1,851 +1,851 @@
-<!DOCTYPE html>
-<html lang="en">
-
-<head>
-  <meta charset="UTF-8" />
-  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>Document</title>
-</head>
-
-<body>
-  <div id="body">
-    <style>
-       body {
-    /* Add the background image */
-    background-image: url('mu-logo-mood-03.jpg');
-    /* Adjust background properties */
-    background-repeat: no-repeat;
-    background-size: cover; /* or contain, depending on your preference */
-    /* Add other background properties if needed */
-  }
-      .cm-msg-text {
-        overflow-wrap: break-word;
-        /* Break long words */
-        word-wrap: break-word;
-        /* Break long words */
-        word-break: break-word;
-        /* Break words */
-        max-width: 100%;
-        /* Limit maximum width */
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-border-bottom-color-000000 {
-        border-bottom-color: #000000 !important;
-      }
-
-
-      [data-block=list11-v4-1-1-b2] .sw-border-bottom-width-none {
-        border-bottom-width: 0px !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-border-bottom-style-none {
-        border-bottom-style: none !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-border-top-color-000000 {
-        border-top-color: #000000 !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-border-top-width-none {
-        border-top-width: 0px !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-border-top-style-none {
-        border-top-style: none !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-padding-bottom-m {
-        padding-bottom: 3rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-padding-top-m {
-        padding-top: 3rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-background-color-ffffff {
-        background-color: #ffffff !important;
-      }
-
-      #body {
-        z-index: 1999;
-        position: fixed;
-        margin: 0;
-        padding: 0;
-        font-family: "Lato", sans-serif;
-        background-color: #f6f7f9;
-      }
-
-      @mixin chabox-container {
-        display: flex;
-        position: absolute;
-        box-shadow: 5px 5px 25px 0 rgba(46, 61, 73, 0.2);
-        flex-direction: column;
-      }
-
-      @mixin chatbox-header {
-        box-sizing: border-box;
-        display: flex;
-        width: 100%;
-        padding: 16px;
-        color: #fff;
-        background-color: #0360a5;
-        align-items: center;
-        justify-content: space-around;
-      }
-
-      @mixin chatbox-main {
-        box-sizing: border-box;
-        width: 100%;
-        padding: calc(2 * 16px) 16px;
-        line-height: calc(16px + 16px / 2);
-        color: #888;
-        text-align: center;
-      }
-
-      @mixin chatbox-footer {
-        box-sizing: border-box;
-        display: flex;
-        width: 100%;
-        padding: 16px;
-        border-top: 1px solid #ddd;
-        align-items: center;
-        justify-content: space-around;
-      }
-
-      @mixin chatbox-floating-button {
-        position: fixed;
-        bottom: 0;
-        right: 0;
-        width: 52px;
-        height: 52px;
-        color: #fff;
-        background-color: #0360a5;
-        background-position: center center;
-        background-repeat: no-repeat;
-        box-shadow: 12px 15px 20px 0 rgba(46, 61, 73, 0.15);
-        border: 0;
-        border-radius: 50%;
-        cursor: pointer;
-      }
-
-      h1 {
-        margin: 0;
-        font-size: 16px;
-        line-height: 1;
-      }
-
-      button {
-        color: inherit;
-        background-color: transparent;
-        border: 0;
-        outline: 0 !important;
-        cursor: pointer;
-      }
-
-      #center-text {
-        display: flex;
-        flex: 1;
-        flex-direction: column;
-        justify-content: center;
-        align-items: center;
-        height: 100%;
-      }
-
-      #chat-circle {
-        position: fixed;
-        bottom: 50px;
-        z-index: 1999;
-        align-items: center;
-        right: 50px;
-        background: #000;
-        width: 60px;
-        justify-content: center;
-        height: 60px;
-        display: flex;
-        border-radius: 50%;
-        color: white;
-        padding: 8px;
-        cursor: pointer;
-        box-shadow: 0px 3px 16px 0px rgba(0, 0, 0, 0.4),
-          0 3px 1px -2px rgba(0, 0, 0, 0.2), 0 1px 5px 0 rgba(0, 0, 0, 0.12);
-      }
-
-      .btn#my-btn {
-        background: white;
-        padding-top: 13px;
-        padding-bottom: 12px;
-        border-radius: 45px;
-        padding-right: 40px;
-        padding-left: 40px;
-        color: #5865c3;
-      }
-
-      #chat-overlay {
-        background: rgba(255, 255, 255, 0.1);
-        position: absolute;
-        top: 0;
-        left: 0;
-        width: 100%;
-        height: 100%;
-        border-radius: 50%;
-        display: none;
-      }
-
-      .chat-box {
-        display: none;
-        background: #efefef;
-        position: fixed;
-        right: 30px;
-        z-index: 1999;
-        bottom: 50px;
-        width: 350px;
-        max-width: 85vw;
-        max-height: 100vh;
-        border-radius: 5px;
-        /*   box-shadow: 0px 5px 35px 9px #464a92; */
-        box-shadow: 0px 5px 35px 9px #ccc;
-      }
-
-      .chat-box-toggle {
-        float: right;
-        margin-right: 15px;
-        cursor: pointer;
-      }
-
-      .chat-box-header {
-        background: #000;
-        height: 70px;
-        border-top-left-radius: 5px;
-        border-top-right-radius: 5px;
-        color: white;
-        text-align: center;
-        font-size: 20px;
-        padding-top: 17px;
-        padding-left: 36px;
-      }
-
-      .chat-box-body {
-        position: relative;
-        height: 370px;
-        height: auto;
-        border: 1px solid #ccc;
-        overflow: hidden;
-      }
-
-      .chat-box-body:after {
-        content: "";
-        background: #fff;
-        opacity: 0.1;
-        top: 0;
-        left: 0;
-        bottom: 0;
-        right: 0;
-        height: 100%;
-        position: absolute;
-        z-index: -1;
-      }
-
-      #chat_input {
-        background: #f4f7f9;
-        width: 100%;
-        position: relative;
-        height: 47px;
-        padding-top: 10px;
-        padding-right: 50px;
-        padding-bottom: 10px;
-        padding-left: 15px;
-        border: none;
-        resize: none;
-        outline: none;
-        border: 1px solid #ccc;
-        color: #888;
-        border-top: none;
-        border-bottom-right-radius: 5px;
-        border-bottom-left-radius: 5px;
-        overflow: hidden;
-      }
-
-      .chat_input>form {
-        margin-bottom: 0;
-      }
-
-      #chat_input::-webkit-input-placeholder {
-        /* Chrome/Opera/Safari */
-        color: #ccc;
-      }
-
-      #chat_input::-moz-placeholder {
-        /* Firefox 19+ */
-        color: #ccc;
-      }
-
-      #chat_input:-ms-input-placeholder {
-        /* IE 10+ */
-        color: #ccc;
-      }
-
-      #chat_input:-moz-placeholder {
-        /* Firefox 18- */
-        color: #ccc;
-      }
-
-      .chat-submit {
-        position: absolute;
-        bottom: 3px;
-        right: 10px;
-        background: transparent;
-        box-shadow: none;
-        border: none;
-        border-radius: 50%;
-        color: #000;
-        width: 35px;
-        height: 35px;
-      }
-
-      .chat_logs {
-        padding: 15px;
-        height: 370px;
-        overflow-y: scroll;
-        margin-bottom: 48px;
-      }
-
-      .chat_logs::-webkit-scrollbar-track {
-        -webkit-box-shadow: inset 0 0 6px rgba(0, 0, 0, 0.3);
-        background-color: #f5f5f5;
-      }
-
-      .chat_logs::-webkit-scrollbar {
-        width: 5px;
-        background-color: #f5f5f5;
-      }
-
-      .chat_logs::-webkit-scrollbar-thumb {
-        background-color: #000;
-      }
-
-      @media only screen and (max-width: 500px) {
-        .chat_logs {
-          height: 40vh;
-        }
-      }
-
-      .chat-msg.user>.msg-avatar img {
-        width: 45px;
-        height: 45px;
-        border-radius: 50%;
-        float: left;
-        width: 15%;
-      }
-
-      .chat-msg.self>.msg-avatar img {
-        width: 45px;
-        height: 45px;
-        border-radius: 50%;
-        float: right;
-        width: 15%;
-      }
-
-      .cm-msg-text {
-        z-index: 1999;
-        background: white;
-        padding: 10px 15px 10px 15px;
-        color: #666;
-        max-width: 75%;
-        float: left;
-        margin-left: 10px;
-        position: relative;
-        margin-bottom: 20px;
-        border-radius: 30px;
-        border-bottom-left-radius: 0px;
-      }
-
-      .svg-bot {
-        height: 24px;
-        width: 24px;
-      }
-
-      .chat-msg {
-        clear: both;
-        z-index: 1999;
-      }
-
-      .chat-msg.self>.cm-msg-text {
-        float: right;
-        margin-right: 10px;
-        border-radius: 30px;
-        border-bottom-right-radius: 0px;
-        background: #000;
-        color: white;
-      }
-
-      .cm-msg-button>ul>li {
-        list-style: none;
-        float: left;
-        width: 50%;
-      }
-
-      .cm-msg-button {
-        clear: both;
-        margin-bottom: 70px;
-      }
-
-      .chat-btn {
-        z-index: 1999;
-      }
-
-      .cancel {
-        display: none;
-        border: none;
-        border-radius: 5px;
-        background-color: #d6d6d6;
-        color: black;
-        z-index: 1999;
-        bottom: 3px;
-        position: absolute;
-        padding: 5px 10px;
-        margin: 0 108px;
-      }
-
-      [data-block^="list11-v4-1-1"] .vertical-list-item {
-        height: 100%;
-        overflow: hidden;
-      }
-
-      [data-block^="list11-v4-1-1"] .additional-elements-wrapper {
-        width: 100%;
-      }
-
-      [data-block^="list11-v4-1-1"] .label-wrapper.vertical {
-        flex-direction: column;
-      }
-
-      [data-block^="list11-v4-1-1"] .label-wrapper {
-        display: flex;
-      }
-
-      p,
-      h1,
-      h2,
-      h3,
-      h4,
-      h5,
-      small {
-        white-space: pre-line;
-      }
-
-      h1,
-      h2,
-      h3,
-      h4,
-      h5,
-      h6,
-      .h1,
-      .h2,
-      .h3,
-      .h4,
-      .h5,
-      .h6 {
-        margin-bottom: 0.25rem;
-        font-family: inherit;
-        font-weight: 400;
-        line-height: 1.1;
-        color: inherit;
-      }
-
-      [data-block^="list11-v4-1-1"] .list-container dl,
-      [data-block^="list11-v4-1-1"] .list-container h1,
-      [data-block^="list11-v4-1-1"] .list-container h2,
-      [data-block^="list11-v4-1-1"] .list-container h3,
-      [data-block^="list11-v4-1-1"] .list-container h4,
-      [data-block^="list11-v4-1-1"] .list-container h5,
-      [data-block^="list11-v4-1-1"] .list-container h6,
-      [data-block^="list11-v4-1-1"] .list-container ol,
-      [data-block^="list11-v4-1-1"] .list-container p,
-      [data-block^="list11-v4-1-1"] .list-container ul {
-        margin: 0;
-        padding: 0;
-      }
-
-
-      [data-block=list11-v4-1-1-b2] .sw-text-align-center {
-        text-align: center !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-margin-top-none {
-        margin-top: 0rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-margin-bottom-none {
-        margin-bottom: 0rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-font-size-2xl {
-        font-size: 1.5rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-padding-bottom-5xs {
-        padding-bottom: 0.75rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-padding-top-none {
-        padding-top: 0rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-letter-spacing-normal {
-        letter-spacing: 0rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-text-color-0A0A0A {
-        color: #0A0A0A !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-padding-right-none {
-        padding-right: 0rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-padding-left-none {
-        padding-left: 0rem !important;
-      }
-
-      [data-block=list11-v4-1-1-b2] .sw-font-weight-semibold {
-        font-weight: 600 !important;
-      }
-
-      h1.sw-text-color-default,
-      h2.sw-text-color-default,
-      h3.sw-font-family-default {
-        color: #000000;
-      }
-
-      h1.sw-font-weight-default,
-      h2.sw-font-weight-default,
-      h3.sw-font-family-default {
-        font-weight: 600;
-      }
-
-      h1.sw-font-family-default,
-      h2.sw-font-family-default,
-      h3.sw-font-family-default {
-        font-family: "IBM Plex Sans";
-      }
-
-      /* .bg-img {
-        background-image: url("./MU_LOGO_BLACK.jpg");
-        background-repeat: no-repeat;
-        position: relative;
-        background-position: center;
-        background-size: cover;
-      } */
-
-      .marwadi {
-        width: 100vw;
-        height: 100vh;
-        text-align: center;
-        margin: auto;
-        font-size: xx-large;
-        font-weight: bolder;
-        display: flex;
-        flex-direction: column;
-        justify-content: center;
-        align-items: center;
-      }
-     
-      .bestie {
-        margin: 1rem;
-        display: flex;
-        justify-content: center;
-        align-items: center;
-        text-align: center;
-        width: 100vw;
-      }
-      #user_id {
-        width: 20%;
-        height: 2rem;
-        border-radius: 5px;
-        border: 1px solid #000;
-        padding: 0.5rem;
-      }
-    </style>
-    
-      <div class="marwadi">
-        Welcome To AI Bestie
-        <div class="bestie">
-          <label>User Name</label>&nbsp;&nbsp;&nbsp;
-        <input type="text" id="user_id" />
-      </div>
-      </div>
-      <div id="chat-circle" class="btn btn-raised" style="">
-        <div id="chat-overlay"></div>
-        AI
-      </div>
-
-      <div class="chat-box" style="display: none">
-        <div class="chat-box-header">
-          AI Bestie
-          <span class="chat-box-toggle"><i class="material-icons">close</i></span>
-        </div>
-
-        <div class="chat-box-body">
-          <div class="chat-box-overlay"></div>
-          <div class="chat_logs" id="chat_logs"></div>
-          <!--chat-log -->
-          <button type="submit" class="cancel" id="cancel">
-            Stop Response
-          </button>
-        </div>
-        <div class="chat_input">
-          <form>
-            <input type="text" id="chat_input" placeholder="Send a message..." />
-            <button type="submit" class="chat-submit" id="submit">
-              <i class="material-icons">send</i>
-            </button>
-          </form>
-        </div>
-      </div>
-    
-  </div>
-  <!-- Scripts -->
-  <link rel="stylesheet"
-    href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" />
-  <link rel="stylesheet"
-    href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-material-design/4.0.2/bootstrap-material-design.css" />
-  <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons" />
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-alpha.6/js/bootstrap.min.js"></script>
-  <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css"></script>
-  <script
-    src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-material-design/4.0.2/bootstrap-material-design.css"></script>
-  <script id="rendered-js">
-    
-    function chatWidget(id, name, status) {
-      $.ajax({
-        url: "/chatwidget",
-        type: "POST",
-        data: JSON.stringify({
-          user_id: id,
-          status: status,
-          user_name: name,
-        }),
-        headers: {
-          "Content-Type": "application/json",
-        },
-        success: function (data) {
-          if (status == "end") {
-            return true;
-          } else {
-            generate_message(data.response, "user");
-          }
-        },
-      });
-    }
-    //   chatWidget(
-    //     window.logged_in_user.airtable_record_id,
-    //     window.logged_in_user.Name,
-    //     "end"
-    //   );
-    //   setTimeout(() => {
-    //     chatWidget(
-    //       window.logged_in_user.airtable_record_id,
-    //       window.logged_in_user.Name,
-    //       "start"
-    //     );
-    //   }, 300);
-    function generate_message(msg, type) {
-      var str = "";
-      str += "<div id='cm-msg" + "' class=\"chat-msg " + type + '">';
-      str += '          <div class="cm-msg-text">';
-      var lines = msg.includes("\n") ? msg.split("\n") : [msg];
-
-      lines.forEach((line) => {
-        // Check if the line contains a URL
-        var urlRegex = /(https?:\/\/[^\s]+)/g;
-        var messageWithLinks = line.replace(urlRegex, '<a href="$1" target="_blank">$1</a>');
-
-        // Append the line with links to the message string
-        str += messageWithLinks;
-
-        // Only add <br> if not last line
-        if (line !== lines[lines.length - 1]) {
-          str += "<br>";
-        }
-      });
-      str += "          <\/div>";
-      str += "        <\/div>";
-      $(".chat_logs").append(str);
-
-      // Adjust scroll position
-      var chatLogs = document.getElementById("chat_logs");
-      chatLogs.scrollTop = chatLogs.scrollHeight;
-    }
-
-    function error_message(msg, type) {
-      INDEX++;
-      var str = "";
-      str += "<div id='cm-msg" + "' class=\"chat-msg " + type + '">';
-      str += '          <div class="cm-msg-text">';
-      var lines = msg.includes("\n") ? msg.split("\n") : [msg];
-
-      lines.forEach((line) => {
-        str += line;
-
-        // Only add <br> if not last line
-        if (line !== lines[lines.length - 1]) {
-          str += "<br>";
-        }
-      });
-      str += "          <\/div>";
-      str += "        <\/div>";
-      $("#chat_logs").append(str);
-      if (type == "self") {
-        $("#chat_input").val("");
-      }
-      $("#chat_logs")
-        .stop()
-        .animate({ scrollTop: $("#chat_logs")[0].scrollHeight }, 1000);
-    }
-    $(function () {
-      var INDEX = 0;
-      var input = document.getElementById("chat_input");
-      var submitBtn = document.getElementById("submit");
-      var cancelRequest = document.getElementById("cancel");
-      
-      var userId = document.getElementById("user_id");
-      $("#cancel").click(function (e) {
-        input.disabled = false;
-        e.preventDefault();
-        submitBtn.disabled = false;
-        input.style.opacity = 1;
-        cancelRequest.style.display = 'none';
-      });
-
-      $("#submit").click(function (e) {
-        cancelRequest.style.display = 'block';
-        input.disabled = true;
-        input.style.opacity = 0.5;
-        submitBtn.disabled = true;
-        e.preventDefault();
-        var msg = $("#chat_input").val();
-        if (msg.trim() == "") {
-          return false;
-        }
-        generate_message(msg, "self");
-        input.value = "";
-
-        $.ajax({
-          url: "/chatwidget",
-          type: "POST",
-          data: JSON.stringify({
-            query: msg,
-            user_id: userId.value
-            // message: `${msg} | ${window.logged_in_user.airtable_record_id}`,
-          }),
-          headers: {
-            "Content-Type": "application/json",
-            "Access-Control-Allow-Origin": "*",
-            "Access-Control-Allow-Headers": "Content-Type",
-          },
-
-          success: function (data) {
-            console.log(data)
-            /* data.map(item => generate_message(item.text, "user")) */
-            generate_message(data, "user");
-            /* generate_message(data.response, "user"); */
-            input.disabled = false;
-            submitBtn.disabled = false;
-            input.style.opacity = 1;
-            cancelRequest.style.display = 'none';
-          },
-          error: function (data) {
-            error_message(
-              "We are sorry. we can't proceess Your Request Please Try again after some times.",
-              "user"
-            );
-            input.disabled = false;
-            submitBtn.disabled = false;
-            input.style.opacity = 1;
-            cancelRequest.style.display = 'none';
-          },
-        });
-      });
-      $(document).delegate(".chat-btn", "click", function () {
-        var value = $(this).attr("chat-value");
-        var name = $(this).html();
-        $("#chat_input").attr("disabled", false);
-        generate_message(name);
-      });
-
-      $("#chat-circle").click(function () {
-        $("#chat-circle").toggle("scale");
-        $(".chat-box").toggle("scale");
-        cancelRequest.style.display = 'block';
-        input.disabled = true;
-        $.ajax({
-          url: "/start-session",
-          type: "POST",
-          data: JSON.stringify({
-            user_id: userId.value
-            // message: `${msg} | ${window.logged_in_user.airtable_record_id}`,
-          }),
-          headers: {
-            "Content-Type": "application/json",
-          },
-          success: function (data) {
-
-            $.ajax({
-          url: "/chatwidget",
-          type: "POST",
-          data: JSON.stringify({
-            query: "you are starting the conversion, with your best friend. also you know about previous summary and mood summary",
-            user_id: userId.value
-            // message: `${msg} | ${window.logged_in_user.airtable_record_id}`,
-          }),
-          headers: {
-            "Content-Type": "application/json",
-            "Access-Control-Allow-Origin": "*",
-            "Access-Control-Allow-Headers": "Content-Type",
-          },
-
-          success: function (data) {
-            console.log(data)
-            /* data.map(item => generate_message(item.text, "user")) */
-            generate_message(data, "user");
-            /* generate_message(data.response, "user"); */
-            input.disabled = false;
-            submitBtn.disabled = false;
-            input.style.opacity = 1;
-            cancelRequest.style.display = 'none';
-          },
-          error: function (data) {
-            error_message(
-              "We are sorry. we can't proceess Your Request Please Try again after some times.",
-              "user"
-            );
-            input.disabled = false;
-            submitBtn.disabled = false;
-            input.style.opacity = 1;
-            cancelRequest.style.display = 'none';
-          },
-        });
-            if (status == "end") {
-
-              return true;
-            } else {
-              console.log(data.response, "session_id");
-            }
-          },
-        });
-        console.log(userId, userId.value)
-      });
-
-      $(".chat-box-toggle").click(function () {
-        $("#chat-circle").toggle("scale");
-        $(".chat-box").toggle("scale");
-        location.reload();
-      });
-    });
-
-  </script>
-  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.0/jquery.min.js"></script>
-</body>
-
+<!DOCTYPE html>
+<html lang="en">
+
+<head>
+  <meta charset="UTF-8" />
+  <meta name="viewport" content="width=device-width, initial-scale=1.0" />
+  <title>Document</title>
+</head>
+
+<body>
+  <div id="body">
+    <style>
+       body {
+    /* Add the background image */
+    background-image: url('mu-logo-mood-03.jpg');
+    /* Adjust background properties */
+    background-repeat: no-repeat;
+    background-size: cover; /* or contain, depending on your preference */
+    /* Add other background properties if needed */
+  }
+      .cm-msg-text {
+        overflow-wrap: break-word;
+        /* Break long words */
+        word-wrap: break-word;
+        /* Break long words */
+        word-break: break-word;
+        /* Break words */
+        max-width: 100%;
+        /* Limit maximum width */
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-border-bottom-color-000000 {
+        border-bottom-color: #000000 !important;
+      }
+
+
+      [data-block=list11-v4-1-1-b2] .sw-border-bottom-width-none {
+        border-bottom-width: 0px !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-border-bottom-style-none {
+        border-bottom-style: none !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-border-top-color-000000 {
+        border-top-color: #000000 !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-border-top-width-none {
+        border-top-width: 0px !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-border-top-style-none {
+        border-top-style: none !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-padding-bottom-m {
+        padding-bottom: 3rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-padding-top-m {
+        padding-top: 3rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-background-color-ffffff {
+        background-color: #ffffff !important;
+      }
+
+      #body {
+        z-index: 1999;
+        position: fixed;
+        margin: 0;
+        padding: 0;
+        font-family: "Lato", sans-serif;
+        background-color: #f6f7f9;
+      }
+
+      @mixin chabox-container {
+        display: flex;
+        position: absolute;
+        box-shadow: 5px 5px 25px 0 rgba(46, 61, 73, 0.2);
+        flex-direction: column;
+      }
+
+      @mixin chatbox-header {
+        box-sizing: border-box;
+        display: flex;
+        width: 100%;
+        padding: 16px;
+        color: #fff;
+        background-color: #0360a5;
+        align-items: center;
+        justify-content: space-around;
+      }
+
+      @mixin chatbox-main {
+        box-sizing: border-box;
+        width: 100%;
+        padding: calc(2 * 16px) 16px;
+        line-height: calc(16px + 16px / 2);
+        color: #888;
+        text-align: center;
+      }
+
+      @mixin chatbox-footer {
+        box-sizing: border-box;
+        display: flex;
+        width: 100%;
+        padding: 16px;
+        border-top: 1px solid #ddd;
+        align-items: center;
+        justify-content: space-around;
+      }
+
+      @mixin chatbox-floating-button {
+        position: fixed;
+        bottom: 0;
+        right: 0;
+        width: 52px;
+        height: 52px;
+        color: #fff;
+        background-color: #0360a5;
+        background-position: center center;
+        background-repeat: no-repeat;
+        box-shadow: 12px 15px 20px 0 rgba(46, 61, 73, 0.15);
+        border: 0;
+        border-radius: 50%;
+        cursor: pointer;
+      }
+
+      h1 {
+        margin: 0;
+        font-size: 16px;
+        line-height: 1;
+      }
+
+      button {
+        color: inherit;
+        background-color: transparent;
+        border: 0;
+        outline: 0 !important;
+        cursor: pointer;
+      }
+
+      #center-text {
+        display: flex;
+        flex: 1;
+        flex-direction: column;
+        justify-content: center;
+        align-items: center;
+        height: 100%;
+      }
+
+      #chat-circle {
+        position: fixed;
+        bottom: 50px;
+        z-index: 1999;
+        align-items: center;
+        right: 50px;
+        background: #000;
+        width: 60px;
+        justify-content: center;
+        height: 60px;
+        display: flex;
+        border-radius: 50%;
+        color: white;
+        padding: 8px;
+        cursor: pointer;
+        box-shadow: 0px 3px 16px 0px rgba(0, 0, 0, 0.4),
+          0 3px 1px -2px rgba(0, 0, 0, 0.2), 0 1px 5px 0 rgba(0, 0, 0, 0.12);
+      }
+
+      .btn#my-btn {
+        background: white;
+        padding-top: 13px;
+        padding-bottom: 12px;
+        border-radius: 45px;
+        padding-right: 40px;
+        padding-left: 40px;
+        color: #5865c3;
+      }
+
+      #chat-overlay {
+        background: rgba(255, 255, 255, 0.1);
+        position: absolute;
+        top: 0;
+        left: 0;
+        width: 100%;
+        height: 100%;
+        border-radius: 50%;
+        display: none;
+      }
+
+      .chat-box {
+        display: none;
+        background: #efefef;
+        position: fixed;
+        right: 30px;
+        z-index: 1999;
+        bottom: 50px;
+        width: 350px;
+        max-width: 85vw;
+        max-height: 100vh;
+        border-radius: 5px;
+        /*   box-shadow: 0px 5px 35px 9px #464a92; */
+        box-shadow: 0px 5px 35px 9px #ccc;
+      }
+
+      .chat-box-toggle {
+        float: right;
+        margin-right: 15px;
+        cursor: pointer;
+      }
+
+      .chat-box-header {
+        background: #000;
+        height: 70px;
+        border-top-left-radius: 5px;
+        border-top-right-radius: 5px;
+        color: white;
+        text-align: center;
+        font-size: 20px;
+        padding-top: 17px;
+        padding-left: 36px;
+      }
+
+      .chat-box-body {
+        position: relative;
+        height: 370px;
+        height: auto;
+        border: 1px solid #ccc;
+        overflow: hidden;
+      }
+
+      .chat-box-body:after {
+        content: "";
+        background: #fff;
+        opacity: 0.1;
+        top: 0;
+        left: 0;
+        bottom: 0;
+        right: 0;
+        height: 100%;
+        position: absolute;
+        z-index: -1;
+      }
+
+      #chat_input {
+        background: #f4f7f9;
+        width: 100%;
+        position: relative;
+        height: 47px;
+        padding-top: 10px;
+        padding-right: 50px;
+        padding-bottom: 10px;
+        padding-left: 15px;
+        border: none;
+        resize: none;
+        outline: none;
+        border: 1px solid #ccc;
+        color: #888;
+        border-top: none;
+        border-bottom-right-radius: 5px;
+        border-bottom-left-radius: 5px;
+        overflow: hidden;
+      }
+
+      .chat_input>form {
+        margin-bottom: 0;
+      }
+
+      #chat_input::-webkit-input-placeholder {
+        /* Chrome/Opera/Safari */
+        color: #ccc;
+      }
+
+      #chat_input::-moz-placeholder {
+        /* Firefox 19+ */
+        color: #ccc;
+      }
+
+      #chat_input:-ms-input-placeholder {
+        /* IE 10+ */
+        color: #ccc;
+      }
+
+      #chat_input:-moz-placeholder {
+        /* Firefox 18- */
+        color: #ccc;
+      }
+
+      .chat-submit {
+        position: absolute;
+        bottom: 3px;
+        right: 10px;
+        background: transparent;
+        box-shadow: none;
+        border: none;
+        border-radius: 50%;
+        color: #000;
+        width: 35px;
+        height: 35px;
+      }
+
+      .chat_logs {
+        padding: 15px;
+        height: 370px;
+        overflow-y: scroll;
+        margin-bottom: 48px;
+      }
+
+      .chat_logs::-webkit-scrollbar-track {
+        -webkit-box-shadow: inset 0 0 6px rgba(0, 0, 0, 0.3);
+        background-color: #f5f5f5;
+      }
+
+      .chat_logs::-webkit-scrollbar {
+        width: 5px;
+        background-color: #f5f5f5;
+      }
+
+      .chat_logs::-webkit-scrollbar-thumb {
+        background-color: #000;
+      }
+
+      @media only screen and (max-width: 500px) {
+        .chat_logs {
+          height: 40vh;
+        }
+      }
+
+      .chat-msg.user>.msg-avatar img {
+        width: 45px;
+        height: 45px;
+        border-radius: 50%;
+        float: left;
+        width: 15%;
+      }
+
+      .chat-msg.self>.msg-avatar img {
+        width: 45px;
+        height: 45px;
+        border-radius: 50%;
+        float: right;
+        width: 15%;
+      }
+
+      .cm-msg-text {
+        z-index: 1999;
+        background: white;
+        padding: 10px 15px 10px 15px;
+        color: #666;
+        max-width: 75%;
+        float: left;
+        margin-left: 10px;
+        position: relative;
+        margin-bottom: 20px;
+        border-radius: 30px;
+        border-bottom-left-radius: 0px;
+      }
+
+      .svg-bot {
+        height: 24px;
+        width: 24px;
+      }
+
+      .chat-msg {
+        clear: both;
+        z-index: 1999;
+      }
+
+      .chat-msg.self>.cm-msg-text {
+        float: right;
+        margin-right: 10px;
+        border-radius: 30px;
+        border-bottom-right-radius: 0px;
+        background: #000;
+        color: white;
+      }
+
+      .cm-msg-button>ul>li {
+        list-style: none;
+        float: left;
+        width: 50%;
+      }
+
+      .cm-msg-button {
+        clear: both;
+        margin-bottom: 70px;
+      }
+
+      .chat-btn {
+        z-index: 1999;
+      }
+
+      .cancel {
+        display: none;
+        border: none;
+        border-radius: 5px;
+        background-color: #d6d6d6;
+        color: black;
+        z-index: 1999;
+        bottom: 3px;
+        position: absolute;
+        padding: 5px 10px;
+        margin: 0 108px;
+      }
+
+      [data-block^="list11-v4-1-1"] .vertical-list-item {
+        height: 100%;
+        overflow: hidden;
+      }
+
+      [data-block^="list11-v4-1-1"] .additional-elements-wrapper {
+        width: 100%;
+      }
+
+      [data-block^="list11-v4-1-1"] .label-wrapper.vertical {
+        flex-direction: column;
+      }
+
+      [data-block^="list11-v4-1-1"] .label-wrapper {
+        display: flex;
+      }
+
+      p,
+      h1,
+      h2,
+      h3,
+      h4,
+      h5,
+      small {
+        white-space: pre-line;
+      }
+
+      h1,
+      h2,
+      h3,
+      h4,
+      h5,
+      h6,
+      .h1,
+      .h2,
+      .h3,
+      .h4,
+      .h5,
+      .h6 {
+        margin-bottom: 0.25rem;
+        font-family: inherit;
+        font-weight: 400;
+        line-height: 1.1;
+        color: inherit;
+      }
+
+      [data-block^="list11-v4-1-1"] .list-container dl,
+      [data-block^="list11-v4-1-1"] .list-container h1,
+      [data-block^="list11-v4-1-1"] .list-container h2,
+      [data-block^="list11-v4-1-1"] .list-container h3,
+      [data-block^="list11-v4-1-1"] .list-container h4,
+      [data-block^="list11-v4-1-1"] .list-container h5,
+      [data-block^="list11-v4-1-1"] .list-container h6,
+      [data-block^="list11-v4-1-1"] .list-container ol,
+      [data-block^="list11-v4-1-1"] .list-container p,
+      [data-block^="list11-v4-1-1"] .list-container ul {
+        margin: 0;
+        padding: 0;
+      }
+
+
+      [data-block=list11-v4-1-1-b2] .sw-text-align-center {
+        text-align: center !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-margin-top-none {
+        margin-top: 0rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-margin-bottom-none {
+        margin-bottom: 0rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-font-size-2xl {
+        font-size: 1.5rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-padding-bottom-5xs {
+        padding-bottom: 0.75rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-padding-top-none {
+        padding-top: 0rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-letter-spacing-normal {
+        letter-spacing: 0rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-text-color-0A0A0A {
+        color: #0A0A0A !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-padding-right-none {
+        padding-right: 0rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-padding-left-none {
+        padding-left: 0rem !important;
+      }
+
+      [data-block=list11-v4-1-1-b2] .sw-font-weight-semibold {
+        font-weight: 600 !important;
+      }
+
+      h1.sw-text-color-default,
+      h2.sw-text-color-default,
+      h3.sw-font-family-default {
+        color: #000000;
+      }
+
+      h1.sw-font-weight-default,
+      h2.sw-font-weight-default,
+      h3.sw-font-family-default {
+        font-weight: 600;
+      }
+
+      h1.sw-font-family-default,
+      h2.sw-font-family-default,
+      h3.sw-font-family-default {
+        font-family: "IBM Plex Sans";
+      }
+
+      /* .bg-img {
+        background-image: url("./MU_LOGO_BLACK.jpg");
+        background-repeat: no-repeat;
+        position: relative;
+        background-position: center;
+        background-size: cover;
+      } */
+
+      .marwadi {
+        width: 100vw;
+        height: 100vh;
+        text-align: center;
+        margin: auto;
+        font-size: xx-large;
+        font-weight: bolder;
+        display: flex;
+        flex-direction: column;
+        justify-content: center;
+        align-items: center;
+      }
+     
+      .bestie {
+        margin: 1rem;
+        display: flex;
+        justify-content: center;
+        align-items: center;
+        text-align: center;
+        width: 100vw;
+      }
+      #user_id {
+        width: 20%;
+        height: 2rem;
+        border-radius: 5px;
+        border: 1px solid #000;
+        padding: 0.5rem;
+      }
+    </style>
+    
+      <div class="marwadi">
+        Welcome To AI Bestie
+        <div class="bestie">
+          <label>User Name</label>&nbsp;&nbsp;&nbsp;
+        <input type="text" id="user_id" />
+      </div>
+      </div>
+      <div id="chat-circle" class="btn btn-raised" style="">
+        <div id="chat-overlay"></div>
+        AI
+      </div>
+
+      <div class="chat-box" style="display: none">
+        <div class="chat-box-header">
+          AI Bestie
+          <span class="chat-box-toggle"><i class="material-icons">close</i></span>
+        </div>
+
+        <div class="chat-box-body">
+          <div class="chat-box-overlay"></div>
+          <div class="chat_logs" id="chat_logs"></div>
+          <!--chat-log -->
+          <button type="submit" class="cancel" id="cancel">
+            Stop Response
+          </button>
+        </div>
+        <div class="chat_input">
+          <form>
+            <input type="text" id="chat_input" placeholder="Send a message..." />
+            <button type="submit" class="chat-submit" id="submit">
+              <i class="material-icons">send</i>
+            </button>
+          </form>
+        </div>
+      </div>
+    
+  </div>
+  <!-- Scripts -->
+  <link rel="stylesheet"
+    href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css" />
+  <link rel="stylesheet"
+    href="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-material-design/4.0.2/bootstrap-material-design.css" />
+  <link rel="stylesheet" href="https://fonts.googleapis.com/icon?family=Material+Icons" />
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.1.1/jquery.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-alpha.6/js/bootstrap.min.js"></script>
+  <script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/4.0.0-alpha.6/css/bootstrap.min.css"></script>
+  <script
+    src="https://cdnjs.cloudflare.com/ajax/libs/bootstrap-material-design/4.0.2/bootstrap-material-design.css"></script>
+  <script id="rendered-js">
+    
+    function chatWidget(id, name, status) {
+      $.ajax({
+        url: "/chatwidget",
+        type: "POST",
+        data: JSON.stringify({
+          user_id: id,
+          status: status,
+          user_name: name,
+        }),
+        headers: {
+          "Content-Type": "application/json",
+        },
+        success: function (data) {
+          if (status == "end") {
+            return true;
+          } else {
+            generate_message(data.response, "user");
+          }
+        },
+      });
+    }
+    //   chatWidget(
+    //     window.logged_in_user.airtable_record_id,
+    //     window.logged_in_user.Name,
+    //     "end"
+    //   );
+    //   setTimeout(() => {
+    //     chatWidget(
+    //       window.logged_in_user.airtable_record_id,
+    //       window.logged_in_user.Name,
+    //       "start"
+    //     );
+    //   }, 300);
+    function generate_message(msg, type) {
+      var str = "";
+      str += "<div id='cm-msg" + "' class=\"chat-msg " + type + '">';
+      str += '          <div class="cm-msg-text">';
+      var lines = msg.includes("\n") ? msg.split("\n") : [msg];
+
+      lines.forEach((line) => {
+        // Check if the line contains a URL
+        var urlRegex = /(https?:\/\/[^\s]+)/g;
+        var messageWithLinks = line.replace(urlRegex, '<a href="$1" target="_blank">$1</a>');
+
+        // Append the line with links to the message string
+        str += messageWithLinks;
+
+        // Only add <br> if not last line
+        if (line !== lines[lines.length - 1]) {
+          str += "<br>";
+        }
+      });
+      str += "          <\/div>";
+      str += "        <\/div>";
+      $(".chat_logs").append(str);
+
+      // Adjust scroll position
+      var chatLogs = document.getElementById("chat_logs");
+      chatLogs.scrollTop = chatLogs.scrollHeight;
+    }
+
+    function error_message(msg, type) {
+      INDEX++;
+      var str = "";
+      str += "<div id='cm-msg" + "' class=\"chat-msg " + type + '">';
+      str += '          <div class="cm-msg-text">';
+      var lines = msg.includes("\n") ? msg.split("\n") : [msg];
+
+      lines.forEach((line) => {
+        str += line;
+
+        // Only add <br> if not last line
+        if (line !== lines[lines.length - 1]) {
+          str += "<br>";
+        }
+      });
+      str += "          <\/div>";
+      str += "        <\/div>";
+      $("#chat_logs").append(str);
+      if (type == "self") {
+        $("#chat_input").val("");
+      }
+      $("#chat_logs")
+        .stop()
+        .animate({ scrollTop: $("#chat_logs")[0].scrollHeight }, 1000);
+    }
+    $(function () {
+      var INDEX = 0;
+      var input = document.getElementById("chat_input");
+      var submitBtn = document.getElementById("submit");
+      var cancelRequest = document.getElementById("cancel");
+      
+      var userId = document.getElementById("user_id");
+      $("#cancel").click(function (e) {
+        input.disabled = false;
+        e.preventDefault();
+        submitBtn.disabled = false;
+        input.style.opacity = 1;
+        cancelRequest.style.display = 'none';
+      });
+
+      $("#submit").click(function (e) {
+        cancelRequest.style.display = 'block';
+        input.disabled = true;
+        input.style.opacity = 0.5;
+        submitBtn.disabled = true;
+        e.preventDefault();
+        var msg = $("#chat_input").val();
+        if (msg.trim() == "") {
+          return false;
+        }
+        generate_message(msg, "self");
+        input.value = "";
+
+        $.ajax({
+          url: "/chatwidget",
+          type: "POST",
+          data: JSON.stringify({
+            query: msg,
+            user_id: userId.value
+            // message: `${msg} | ${window.logged_in_user.airtable_record_id}`,
+          }),
+          headers: {
+            "Content-Type": "application/json",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Content-Type",
+          },
+
+          success: function (data) {
+            console.log(data)
+            /* data.map(item => generate_message(item.text, "user")) */
+            generate_message(data, "user");
+            /* generate_message(data.response, "user"); */
+            input.disabled = false;
+            submitBtn.disabled = false;
+            input.style.opacity = 1;
+            cancelRequest.style.display = 'none';
+          },
+          error: function (data) {
+            error_message(
+              "We are sorry. we can't proceess Your Request Please Try again after some times.",
+              "user"
+            );
+            input.disabled = false;
+            submitBtn.disabled = false;
+            input.style.opacity = 1;
+            cancelRequest.style.display = 'none';
+          },
+        });
+      });
+      $(document).delegate(".chat-btn", "click", function () {
+        var value = $(this).attr("chat-value");
+        var name = $(this).html();
+        $("#chat_input").attr("disabled", false);
+        generate_message(name);
+      });
+
+      $("#chat-circle").click(function () {
+        $("#chat-circle").toggle("scale");
+        $(".chat-box").toggle("scale");
+        cancelRequest.style.display = 'block';
+        input.disabled = true;
+        $.ajax({
+          url: "/start-session",
+          type: "POST",
+          data: JSON.stringify({
+            user_id: userId.value
+            // message: `${msg} | ${window.logged_in_user.airtable_record_id}`,
+          }),
+          headers: {
+            "Content-Type": "application/json",
+          },
+          success: function (data) {
+
+            $.ajax({
+          url: "/chatwidget",
+          type: "POST",
+          data: JSON.stringify({
+            query: "START",
+            user_id: userId.value
+            // message: `${msg} | ${window.logged_in_user.airtable_record_id}`,
+          }),
+          headers: {
+            "Content-Type": "application/json",
+            "Access-Control-Allow-Origin": "*",
+            "Access-Control-Allow-Headers": "Content-Type",
+          },
+
+          success: function (data) {
+            console.log(data)
+            /* data.map(item => generate_message(item.text, "user")) */
+            generate_message(data, "user");
+            /* generate_message(data.response, "user"); */
+            input.disabled = false;
+            submitBtn.disabled = false;
+            input.style.opacity = 1;
+            cancelRequest.style.display = 'none';
+          },
+          error: function (data) {
+            error_message(
+              "We are sorry. we can't proceess Your Request Please Try again after some times.",
+              "user"
+            );
+            input.disabled = false;
+            submitBtn.disabled = false;
+            input.style.opacity = 1;
+            cancelRequest.style.display = 'none';
+          },
+        });
+            if (status == "end") {
+
+              return true;
+            } else {
+              console.log(data.response, "session_id");
+            }
+          },
+        });
+        console.log(userId, userId.value)
+      });
+
+      $(".chat-box-toggle").click(function () {
+        $("#chat-circle").toggle("scale");
+        $(".chat-box").toggle("scale");
+        location.reload();
+      });
+    });
+
+  </script>
+  <script src="https://ajax.googleapis.com/ajax/libs/jquery/3.6.0/jquery.min.js"></script>
+</body>
+
 </html>

templates/index.html

@@ -1,13 +1,13 @@
-
-<!DOCTYPE html>
-<html lang="en">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>FastAPI HTML Example</title>
-</head>
-<body>
-    <h1>Hello, FastAPI!</h1>
-    <p>This is an example of serving HTML content with FastAPI.</p>
-</body>
-</html>
+
+<!DOCTYPE html>
+<html lang="en">
+<head>
+    <meta charset="UTF-8">
+    <meta name="viewport" content="width=device-width, initial-scale=1.0">
+    <title>FastAPI HTML Example</title>
+</head>
+<body>
+    <h1>Hello, FastAPI!</h1>
+    <p>This is an example of serving HTML content with FastAPI.</p>
+</body>
+</html>

tools.py

@@ -1,350 +1,350 @@
-import os
-from langchain.agents import tool
-from langchain_community.chat_models import ChatOpenAI
-import pandas as pd
-
-from config import settings
-
-
-
-def get_embeddings(text_list):
-    encoded_input = settings.tokenizer(
-        text_list, padding=True, truncation=True, return_tensors="pt"
-    )
-    # encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
-    encoded_input = {k: v for k, v in encoded_input.items()}
-    model_output = settings.model(**encoded_input)
-    
-    cls_pool = model_output.last_hidden_state[:, 0]
-    return cls_pool
-
-def reg(chat):
-  question_embedding = get_embeddings([chat]).cpu().detach().numpy()
-  scores, samples = settings.dataset.get_nearest_examples(
-      "embeddings", question_embedding, k=5
-  )
-  samples_df = pd.DataFrame.from_dict(samples)
-  print(samples_df.columns)
-  samples_df["scores"] = scores
-  samples_df.sort_values("scores", ascending=False, inplace=True)
-  return samples_df[['title', 'cover_image', 'referral_link', 'category_id']]
-
-
-@tool("MOXICASTS-questions", return_direct=True)
-def moxicast(prompt: str) -> str:
-    """this function is used when user wants to know about MOXICASTS feature.MOXICASTS is a feature of BMoxi for Advice and guidance on life topics.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MOXICASTS is a feature of BMoxi for Advice and guidance on life topics."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-@tool("PEP-TALKPODS-questions", return_direct=True)
-def peptalks(prompt: str) -> str:
-    """this function is used when user wants to know about PEP TALK PODS feature.PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PEP TALK PODS: Quick audio pep talks for boosting mood and motivation."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-@tool("SOCIAL-SANCTUARY-questions", return_direct=True)
-def sactury(prompt: str) -> str:
-    """this function is used when user wants to know about SOCIAL SANCTUARY feature.THE SOCIAL SANCTUARY Anonymous community forum for support and sharing.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. THE SOCIAL SANCTUARY Anonymous community forum for support and sharing."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-@tool("POWER-ZENS-questions", return_direct=True)
-def power_zens(prompt: str) -> str:
-    """this function is used when user wants to know about POWER ZENS feature. POWER ZENS Mini meditations for emotional control.
-
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. POWER ZENS Mini meditations for emotional control."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-@tool("MY-CALENDAR-questions", return_direct=True)
-def my_calender(prompt: str) -> str:
-    """this function is used when user wants to know about MY CALENDAR feature.MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY CALENDAR: Visual calendar for tracking self-care rituals and moods."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-
-@tool("PUSH-AFFIRMATIONS-questions", return_direct=True)
-def affirmations(prompt: str) -> str:
-    """this function is used when user wants to know about PUSH AFFIRMATIONS feature.PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PUSH AFFIRMATIONS: Daily text affirmations for positive thinking."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-@tool("HOROSCOPE-questions", return_direct=True)
-def horoscope(prompt: str) -> str:
-    """this function is used when user wants to know about HOROSCOPE feature.SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-@tool("INFLUENCER-POSTS-questions", return_direct=True)
-def influencer_post(prompt: str) -> str:
-    """this function is used when user wants to know about INFLUENCER POSTS feature.INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon)."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-@tool("MY-VIBECHECK-questions", return_direct=True)
-def my_vibecheck(prompt: str) -> str:
-    """this function is used when user wants to know about MY VIBECHECK feature. MY VIBECHECK: Monitor and understand emotional patterns.
-
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY VIBECHECK: Monitor and understand emotional patterns."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-@tool("MY-RITUALS-questions", return_direct=True)
-def my_rituals(prompt: str) -> str:
-    """this function is used when user wants to know about MY RITUALS feature.MY RITUALS: Create personalized self-care routines.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY RITUALS: Create personalized self-care routines."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-
-@tool("MY-REWARDS-questions", return_direct=True)
-def my_rewards(prompt: str) -> str:
-    """this function is used when user wants to know about MY REWARDS feature.MY REWARDS: Earn points for self-care, redeemable for gift cards.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY REWARDS: Earn points for self-care, redeemable for gift cards."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-@tool("mentoring-questions", return_direct=True)
-def mentoring(prompt: str) -> str:
-    """this function is used when user wants to know about 1-1 mentoring feature.  1:1 MENTORING: Personalized mentoring (coming soon).
-
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.  1:1 MENTORING: Personalized mentoring (coming soon)."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-
-
-@tool("MY-JOURNAL-questions", return_direct=True)
-def my_journal(prompt: str) -> str:
-    """this function is used when user wants to know about MY JOURNAL feature.MY JOURNAL: Guided journaling exercises for self-reflection.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY JOURNAL: Guided journaling exercises for self-reflection."
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you are going to make answer only using this context not use any other information
-   context : {context}
-    Input: {input}
-    """
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-@tool("recommandation_tool", return_direct=True)
-def recommand_podcast(prompt: str) -> str:
-    """ this function is used when your best friend want any recommandation and tips. also you feel that this is the best time for any recommandation or your friend.
-    Args:
-        prompt (string): user query
-
-    Returns:
-        string: answer of the query
-    """
-    df = reg(prompt)
-    context = """"""
-    for index, row in df.iterrows():
-        'title', 'cover_image', 'referral_link', 'category_id'
-        context+= f"Row {index + 1}: Title: {row['title']} image: {row['cover_image']} referral_link: {row['referral_link']} category_id: {row['category_id']}"
-    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-    # Define the system prompt
-    system_template = """ you have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast.
-    you must use the context only not any other information.
-    context : {context}
-    """
-    print(system_template.format(context=context, input=prompt))
-    response = llm.invoke(system_template.format(context=context, input=prompt))
-
-    return response.content
-
-@tool("set-chat-bot-name", return_direct=True)
-def set_chatbot_name(name: str) -> str:
-    """ this function is used when your best friend want to give you new name.
-    Args:
-        name (string): new name of you.
-
-    Returns:
-        string: response after setting new name.
-    """
-
-    return "Okay, from now my name will be "+ name 
+import os
+from langchain.agents import tool
+from langchain_community.chat_models import ChatOpenAI
+import pandas as pd
+
+from config import settings
+
+
+
+def get_embeddings(text_list):
+    encoded_input = settings.tokenizer(
+        text_list, padding=True, truncation=True, return_tensors="pt"
+    )
+    # encoded_input = {k: v.to(device) for k, v in encoded_input.items()}
+    encoded_input = {k: v for k, v in encoded_input.items()}
+    model_output = settings.model(**encoded_input)
+    
+    cls_pool = model_output.last_hidden_state[:, 0]
+    return cls_pool
+
+def reg(chat):
+  question_embedding = get_embeddings([chat]).cpu().detach().numpy()
+  scores, samples = settings.dataset.get_nearest_examples(
+      "embeddings", question_embedding, k=5
+  )
+  samples_df = pd.DataFrame.from_dict(samples)
+  # print(samples_df.columns)
+  samples_df["scores"] = scores
+  samples_df.sort_values("scores", ascending=False, inplace=True)
+  return samples_df[['title', 'cover_image', 'referral_link', 'category_id']]
+
+
+@tool("MOXICASTS-questions", return_direct=True)
+def moxicast(prompt: str) -> str:
+    """this function is used when user wants to know about MOXICASTS feature.MOXICASTS is a feature of BMoxi for Advice and guidance on life topics.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MOXICASTS is a feature of BMoxi for Advice and guidance on life topics."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("PEP-TALKPODS-questions", return_direct=True)
+def peptalks(prompt: str) -> str:
+    """this function is used when user wants to know about PEP TALK PODS feature.PEP TALK PODS: Quick audio pep talks for boosting mood and motivation.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PEP TALK PODS: Quick audio pep talks for boosting mood and motivation."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("SOCIAL-SANCTUARY-questions", return_direct=True)
+def sactury(prompt: str) -> str:
+    """this function is used when user wants to know about SOCIAL SANCTUARY feature.THE SOCIAL SANCTUARY Anonymous community forum for support and sharing.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. THE SOCIAL SANCTUARY Anonymous community forum for support and sharing."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+@tool("POWER-ZENS-questions", return_direct=True)
+def power_zens(prompt: str) -> str:
+    """this function is used when user wants to know about POWER ZENS feature. POWER ZENS Mini meditations for emotional control.
+
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. POWER ZENS Mini meditations for emotional control."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("MY-CALENDAR-questions", return_direct=True)
+def my_calender(prompt: str) -> str:
+    """this function is used when user wants to know about MY CALENDAR feature.MY CALENDAR: Visual calendar for tracking self-care rituals and moods.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY CALENDAR: Visual calendar for tracking self-care rituals and moods."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+
+@tool("PUSH-AFFIRMATIONS-questions", return_direct=True)
+def affirmations(prompt: str) -> str:
+    """this function is used when user wants to know about PUSH AFFIRMATIONS feature.PUSH AFFIRMATIONS: Daily text affirmations for positive thinking.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. PUSH AFFIRMATIONS: Daily text affirmations for positive thinking."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("HOROSCOPE-questions", return_direct=True)
+def horoscope(prompt: str) -> str:
+    """this function is used when user wants to know about HOROSCOPE feature.SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. SELF-LOVE HOROSCOPE: Weekly personalized horoscope readings."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("INFLUENCER-POSTS-questions", return_direct=True)
+def influencer_post(prompt: str) -> str:
+    """this function is used when user wants to know about INFLUENCER POSTS feature.INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon).
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. INFLUENCER POSTS: Exclusive access to social media influencer advice (coming soon)."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+@tool("MY-VIBECHECK-questions", return_direct=True)
+def my_vibecheck(prompt: str) -> str:
+    """this function is used when user wants to know about MY VIBECHECK feature. MY VIBECHECK: Monitor and understand emotional patterns.
+
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY VIBECHECK: Monitor and understand emotional patterns."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("MY-RITUALS-questions", return_direct=True)
+def my_rituals(prompt: str) -> str:
+    """this function is used when user wants to know about MY RITUALS feature.MY RITUALS: Create personalized self-care routines.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY RITUALS: Create personalized self-care routines."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+
+@tool("MY-REWARDS-questions", return_direct=True)
+def my_rewards(prompt: str) -> str:
+    """this function is used when user wants to know about MY REWARDS feature.MY REWARDS: Earn points for self-care, redeemable for gift cards.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY REWARDS: Earn points for self-care, redeemable for gift cards."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+@tool("mentoring-questions", return_direct=True)
+def mentoring(prompt: str) -> str:
+    """this function is used when user wants to know about 1-1 mentoring feature.  1:1 MENTORING: Personalized mentoring (coming soon).
+
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times.  1:1 MENTORING: Personalized mentoring (coming soon)."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+
+
+@tool("MY-JOURNAL-questions", return_direct=True)
+def my_journal(prompt: str) -> str:
+    """this function is used when user wants to know about MY JOURNAL feature.MY JOURNAL: Guided journaling exercises for self-reflection.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    context = "BMOXI app is designed for teenage girls where they can listen some musics explore some contents had 1:1 mentoring sessions with all above features for helping them in their hard times. MY JOURNAL: Guided journaling exercises for self-reflection."
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you are going to make answer only using this context not use any other information
+   context : {context}
+    Input: {input}
+    """
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("recommandation_tool", return_direct=True)
+def recommand_podcast(prompt: str) -> str:
+    """ this function is used when your best friend want any recommandation and tips. also you feel that this is the best time for any recommandation or your friend.
+    Args:
+        prompt (string): user query
+
+    Returns:
+        string: answer of the query
+    """
+    df = reg(prompt)
+    context = """"""
+    for index, row in df.iterrows():
+        'title', 'cover_image', 'referral_link', 'category_id'
+        context+= f"Row {index + 1}: Title: {row['title']} image: {row['cover_image']} referral_link: {row['referral_link']} category_id: {row['category_id']}"
+    llm = ChatOpenAI(model=settings.OPENAI_MODEL, openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+    # Define the system prompt
+    system_template = """ you have to give the recommandation of podcast for: {input}. also you are giving referal link of podcast.
+    you must use the context only not any other information.
+    context : {context}
+    """
+    # print(system_template.format(context=context, input=prompt))
+    response = llm.invoke(system_template.format(context=context, input=prompt))
+
+    return response.content
+
+@tool("set-chat-bot-name", return_direct=True)
+def set_chatbot_name(name: str) -> str:
+    """ this function is used when your best friend want to give you new name.
+    Args:
+        name (string): new name of you.
+
+    Returns:
+        string: response after setting new name.
+    """
+
+    return "Okay, from now my name will be "+ name 

utils.py

@@ -1,174 +1,103 @@
-import json
-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 langchain_mongodb.chat_message_histories import MongoDBChatMessageHistory
-
-from tools import moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name
-
-
-def get_last_session(user_id="user_1"):
-    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
-        settings.MONGODB_DB_USER_SESSIONS_COLLECTION_NAME)
-
-    sessions_cursor = mongodb_chatbot_message_collection.find_one(
-        {"user_id": user_id})
-
-    print(sessions_cursor)
-    sessions_list = sessions_cursor['session_id']
-
-    second_last_session_id = None
-    if len(sessions_list) >= 2:
-        second_last_session_id = sessions_list[-2]
-
-    return {"last_session_id": sessions_list[-1], "second_last_session_id": second_last_session_id if second_last_session_id else None}
-
-
-def get_mood_summary(data='''"35","27","mood_tracker","[{""question_id"":1,""question"":""my vibe rn is\u2026"",""answer"":[""Sad""],""time"":""5:12 PM""},{""question_id"":2,""question"":""I feel this way bc of\u2026 "",""answer"":[""SCHOOL""],""time"":""5:12 PM""}]","2022-11-02 17:12:42","2024-03-28 07:27:13"'''):
-    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=0.7)
-    return llm.invoke(system_prompt.format(data=data)).content
-
-
-def get_chat_history(session_id="bmoxinew"):
-    # Set up MongoDB for storing chat history
-    chat_history = MongoDBChatMessageHistory(
-        connection_string=settings.MONGODB_CONNECTION_STRING,
-        database_name=settings.MONGODB_DB_NAME,  # Specify the database name here
-        collection_name=settings.MONGODB_DB_CHAT_COLLECTION_NAME,
-        session_id=session_id,
-    )
-
-    return chat_history
-
-
-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_chat_bot_name(user_id="user_1"):
-    print(settings.MONGODB_CONNECTION_STRING)
-    print(settings.mongodb_chatbot_name_collection)
-    result = settings.mongodb_chatbot_name_collection.find_one(
-        {"user_id": user_id})
-
-    print("CHATBOT RESULT", result, type(result))
-    if result:
-        print(result)
-        return result['chat_bot_name']
-    return settings.CHATBOT_NAME
-
-
-def get_last_session_summary(last_session_id, second_last_session_id):
-
-    mongodb_chatbot_message_collection = settings.mongodb_db.get_collection(
-        settings.MONGODB_DB_CHAT_COLLECTION_NAME)
-
-    collection_count = mongodb_chatbot_message_collection.count_documents({"SessionId": last_session_id})
-    print("******************************** data********************888")
-    print(collection_count)
-    print(last_session_id)
-    print("*********************************")
-    if collection_count <=2:
-        sessions_cursor = mongodb_chatbot_message_collection.find({"SessionId": second_last_session_id})  # Sort by timestamp descending and limit to 2 results
-
-        print(sessions_cursor)
-        sessions_list = list(sessions_cursor)
-        print(sessions_list)
-
-        conversation = """"""
-        for document in sessions_list:
-            print("MY document")
-            print(document)
-            if "History" in document:
-                history = json.loads(document['History'])
-                print(history)
-                print(history['type'])
-                print(history['data'])
-                print(history['data']['content'])
-                conversation += f"""{history['type']}: {history['data']['content']}\n"""
-
-        print(conversation)
-        system_prompt = """You are an descripting assistant that provides that analyze user conversation with AI bot and gives problem user was facing and weather that problem was solved or not and give response in below format.
-      
-      conversation: {conversation}
-      
-      problem: 
-      is_problem_solved: YES/NO
-      """
-
-        llm = ChatOpenAI(model=settings.OPENAI_MODEL,
-                         openai_api_key=settings.OPENAI_KEY, temperature=0.7)
-
-        response = llm.invoke(system_prompt.format(conversation=conversation)).content
-        print("********************************* PREVIOUS PROBLEM *******************************************")
-        print(response)
-        return response
-
-    else:
-        return ""
-
-def set_chat_bot_name(name, user_id):
-    # Insert document into collection
-    insert_result = settings.mongodb_chatbot_name_collection.update_one({"user_id": user_id}, { "$set": { "chat_bot_name": name } },  upsert=True)
-    print("done successfully...")
-    return name
-
-def create_agent(user_id):
-    print("get user Id**********************",user_id)
-    tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name]
-    # tools = [moxicast]
-
-    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=settings.TEMPERATURE).bind(functions=functions)
-
-    chat_bot_name = get_chat_bot_name(user_id)
-
-    print("CHABT NAME", chat_bot_name)
-    mood_summary = get_mood_summary()
-
-    previous_session_id = get_last_session(user_id)
-    print(previous_session_id)
-    prevous_problem_summary = None
-    if previous_session_id['second_last_session_id']:
-        prevous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id'])
-
-    print("**************************************** SUMMARY ***********************************************")
-    print(prevous_problem_summary)
-
-    prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood="", previous_summary=prevous_problem_summary)),
-                                               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")
-
-    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 moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name
+
+from database_functions import get_chat_bot_name,get_chat_history, get_last_conversion, get_last_session, get_mood_data,use_tools
+
+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=0.7)
+    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 = """ summarize whole conversation in at max 2 sentence.
+        conversation: {conversation}
+        summary: 
+        """
+
+        llm = ChatOpenAI(model=settings.OPENAI_MODEL,
+                        openai_api_key=settings.OPENAI_KEY, temperature=0.7)
+
+        response = llm.invoke(system_prompt.format(conversation=conversation)).content
+        # print("********************************* PREVIOUS PROBLEM *******************************************")
+        # print(response)
+        return response
+    else:
+        return ""
+
+def create_agent(user_id):
+    # print("get user Id**********************",user_id)
+    
+    previous_session_id = get_last_session(user_id)
+    # print(previous_session_id)
+    if  use_tools(previous_session_id["last_session_id"]):
+        tools = [moxicast, my_calender, my_journal, my_rewards, my_rituals, my_vibecheck, peptalks, sactury, power_zens, affirmations, horoscope, mentoring, influencer_post, recommand_podcast, set_chatbot_name]
+    else:
+        tools = [set_chatbot_name]
+
+    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=settings.TEMPERATURE).bind(functions=functions)
+
+    chat_bot_name = get_chat_bot_name(user_id)
+
+    # print("CHABT NAME", chat_bot_name)
+    start = time.time()
+    mood_summary = get_mood_summary(user_id)
+    prevous_problem_summary = None
+    if previous_session_id['second_last_session_id']:
+        prevous_problem_summary = get_last_session_summary(previous_session_id['last_session_id'], previous_session_id['second_last_session_id'])
+
+    # print("**************************************** SUMMARY ***********************************************")
+    # print(prevous_problem_summary)
+    print("time require for mood summary: ",time.time()-start)
+    prompt = ChatPromptTemplate.from_messages([("system", settings.SYSTEM_PROMPT.format(name = chat_bot_name, mood="", previous_summary=prevous_problem_summary)),
+                                               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")
+
+    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