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app.py

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+import streamlit as st
+import sqlite3
+from hashlib import sha256
+import streamlit as st
+from langchain_community.embeddings import LlamaCppEmbeddings
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import TextLoader
+from langchain_community.vectorstores import FAISS
+from langchain.embeddings import HuggingFaceEmbeddings 
+from langchain.chains.llm import LLMChain
+from langchain_community.llms import LlamaCpp
+from langchain.chains import LLMChain
+from langchain_community.llms import OpenAI
+from langchain_core.prompts import PromptTemplate, ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.documents import Document
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from datetime import date
+
+# Create a SQLite database and table
+conn = sqlite3.connect("user_credentials.db")
+cursor = conn.cursor()
+cursor.execute('''
+    CREATE TABLE IF NOT EXISTS users (
+        username TEXT PRIMARY KEY,
+        password TEXT
+    )
+''')
+conn.commit()
+
+
+if 'embeddings' not in st.session_state:
+    st.session_state.embeddings = HuggingFaceEmbeddings(
+                model_name="sentence-transformers/all-MiniLM-L6-v2",
+                model_kwargs={"device": "cpu"},
+)
+def get_similar_docs(query):
+    db = FAISS.load_local('faiss_index',st.session_state.embeddings)
+    docs = db.similarity_search_with_score(query,100)
+    return docs
+def format_docs(docs):
+        return " ".join(doc.page_content for doc in docs)
+def get_advice_from_llm(query):
+    db = FAISS.load_local(st.session_state.username,st.session_state.embeddings)  
+    retriever = db.as_retriever()
+    llm = LlamaCpp(model_path="./tinyllama-1.1b-chat-v1.0.Q8_0.gguf",n_ctx = 2048)
+    chat_history_str = "\n".join(["<|im_start|>" + entry[0]+ entry[1] +"<|im_emd|>\n" for entry in st.session_state['chat_history']])
+
+    template = """" 
+        <|im_start|>system
+        {context}""" +  chat_history_str + "<|im_end|>"\
+        """
+        <|im_start|>user{input}<|im_end|>
+
+        <|im_start|>assistant
+       """    
+    
+    prompt = PromptTemplate(input_variables=["input","context"], template=template)
+    llm_chain = LLMChain(llm=llm, prompt=prompt)
+
+    rag_chain = ( {"context": retriever|format_docs, "input": RunnablePassthrough()}| llm_chain)
+    answer = rag_chain.invoke(query)
+    return answer
+
+def vectordb_entry():
+    loader = TextLoader(f"./{st.session_state.username}.txt")
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=100, chunk_overlap=10)
+    docs = text_splitter.split_documents(documents)
+    db = FAISS.load_local(st.session_state.username,st.session_state.embeddings)
+    db.add_documents(docs)
+    db.save_local(st.session_state.username)
+
+def save_into_text_file(file_path,text):
+    with open(file_path, 'w') as file:
+        file.write(text)
+    print(f"String saved to {file_path}")
+
+def journal():
+   
+    messages = st.container(height=600)
+    query = st.chat_input("Need some advice?")
+
+    if 'input_key' not in st.session_state:
+        st.session_state.input_key = 0
+
+    if 'chat_history' not in st.session_state:
+        st.session_state.chat_history = []
+    
+    if query:
+        answer = get_advice_from_llm(query)
+        st.session_state.chat_history.append(("user", query))
+        st.session_state.chat_history.append(("assistant", answer['text']))
+        st.session_state.input_key += 1
+
+    if 'chat_history' in st.session_state and st.session_state.chat_history:
+        for speaker, message in st.session_state.chat_history:
+            if speaker == "user":
+                who = "You"
+            else:
+                who = "JournaLLM"
+
+            messages.chat_message(speaker).write(who + ': '+ str(message))
+
+    if st.button('Reset Chat'):
+        st.session_state.chat_history = []
+        st.session_state.input_key += 1
+        st.experimental_rerun()
+
+
+# Function to hash passwords
+def hash_password(password):
+    return sha256(password.encode()).hexdigest()
+
+# Function to check login credentials
+def authenticate(username, password):
+    hashed_password = hash_password(password)
+    cursor.execute("SELECT * FROM users WHERE username=? AND password=?", (username, hashed_password))
+    return cursor.fetchone() is not None
+
+# Function to add a new user to the database
+def add_user(username, password):
+    hashed_password = hash_password(password)
+    try:
+        cursor.execute("INSERT INTO users (username, password) VALUES (?, ?)", (username, hashed_password))
+        conn.commit()
+        return True  # User added successfully
+    except sqlite3.IntegrityError:
+        return False  # Username already exists
+
+# Streamlit Login Page
+def login_page():
+    st.title("Login Page")
+    un = st.text_input("Username:")
+    pw = st.text_input("Password:", type="password")
+    if un and pw:
+        st.session_state['username'] = un
+        st.session_state['password'] = pw
+    
+    if st.button("Login"):
+        if not st.session_state['username'] or not st.session_state['password']:
+            st.error("Both username and password are required.")
+        elif authenticate(st.session_state['username'], st.session_state['password']):
+            create_table()
+            st.success("Login successful!")
+        else:
+            st.error("Invalid credentials. Please try again.")
+
+# Streamlit Signup Page
+def signup_page():
+    st.title("Signup Page")
+    new_username = st.text_input("New Username:")
+    new_password = st.text_input("New Password:", type="password")
+
+    if st.button("Signup"):
+        if not new_username or not new_password:
+            st.error("Both username and password are required.")
+        else:
+            result = add_user(new_username, new_password)
+            if result:
+                file_path = f"{new_username}.txt"
+                text = "I've started writing my journal"
+                # Open the file in write mode and write the string
+                with open(file_path, 'w') as file:
+                    file.write(text)
+
+                print(f"String saved to {file_path}")
+                loader = TextLoader(f"./{new_username}.txt")
+                documents = loader.load()
+                text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+                docs = text_splitter.split_documents(documents)
+                embeddings = HuggingFaceEmbeddings(
+                                model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                model_kwargs={"device": "cpu"},
+                )
+                db = FAISS.from_documents(docs,embeddings)
+                db.save_local(new_username)
+                st.success("Signup successful! You can now login.")
+
+            else:
+                st.error("Username already exists. Please choose a different username.") 
+
+
+def create_table():
+    conn = sqlite3.connect(f'{st.session_state.username}_entries.db')
+    cursor = conn.cursor()
+
+    cursor.execute('''
+        CREATE TABLE IF NOT EXISTS entries (
+            id INTEGER PRIMARY KEY AUTOINCREMENT,
+            date TEXT,
+            notes TEXT
+        )
+    ''')
+
+    conn.commit()
+    conn.close()
+
+# Function to insert data into the SQLite database
+def insert_data(date, notes):
+    conn = sqlite3.connect(f'{st.session_state.username}_entries.db')
+    cursor = conn.cursor()
+
+    cursor.execute('''
+        INSERT INTO entries (date, notes)
+        VALUES (?, ?)
+    ''', (date, notes))
+
+    conn.commit()
+    conn.close()
+
+# Function to retrieve data for a selected date
+def retrieve_data(selected_date):
+    conn = sqlite3.connect(f'{st.session_state.username}_entries.db')
+    cursor = conn.cursor()
+
+    cursor.execute('''
+        SELECT date, notes FROM entries WHERE date = ?
+    ''', (selected_date,))
+
+    data = cursor.fetchall()
+
+    conn.close()
+    return data
+
+
+def entry():
+    st.title('JournaLLM')
+    st.write('Welcome to JournaLLM, \
+             your personal space for mindful \
+             reflection and goal tracking! This app is designed to help you \
+             seamlessly capture your daily thoughts, \
+             set meaningful goals, and track your progress.')
+    c1,c2 = st.columns(2)
+    if 'input_key' not in st.session_state:
+        st.session_state.input_key = 0
+    
+    file_path = f"{st.session_state.username}.txt"
+
+    c1.write("Today's Entry")
+    text0 = c1.text_area("Enter text ")
+  
+    # template = f'''Question: What happened on {date.today().strftime("%B %d, %Y")}? 
+    # How did I feel on {date.today().strftime("%B %d, %Y")}? 
+    # What were the events that happened on {date.today().strftime("%B %d, %Y")}? 
+    # Describe your day, {date.today().strftime("%B %d, %Y")}. \n Answer: '''
+    text = f""" <|im_start|>system
+         What happened on {date.today().strftime("%B %d, %Y")}? 
+    How did I feel on {date.today().strftime("%B %d, %Y")}? 
+    What were the events that happened on {date.today().strftime("%B %d, %Y")}? 
+    Describe your day, {date.today().strftime("%B %d, %Y")}.<|im_end|>
+    
+        <|im_start|>user
+        {text0}<|im_end|>"""
+
+
+    if c1.button('Pen down') and text:
+        save_into_text_file(file_path,text)
+        vectordb_entry()
+        c1.write('Entry saved')
+        st.session_state.input_key += 1
+        #display previous entries
+        insert_data(date.today().strftime("%B %d, %Y"), text0)
+
+    #displaying 
+    c2.write('View previous entries')
+    selected_date = c2.date_input('Select a date', date.today())
+    data = retrieve_data(selected_date.strftime("%B %d, %Y"))
+    if data:
+        en = c2.container(height=300)
+        for i in data:
+            en.write(i[1])
+        #[en.write(x[1]) for x in data]
+    else:
+        c2.info('No entries for the selected date.')
+
+
+
+# Main Streamlit App
+def main():
+    st.set_page_config(layout="wide")
+    st.sidebar.title("Navigation")
+    page = st.sidebar.radio("Go to", ["Login", "Signup","Journal","Advice"])
+
+    if page == "Login":
+        login_page()
+    elif page == "Signup":
+        signup_page()
+    elif page == "Journal":
+        if st.session_state.username == "":
+            st.write('Please login to continue.')
+        else:
+            st.write(f"Logged in as {st.session_state.username}")
+            entry()
+    elif page == "Advice":
+        if st.session_state.username == "":
+            st.write('Please login to continue.')
+        else:
+            st.write(f"Logged in as {st.session_state.username}")
+            journal()
+
+if __name__ == "__main__":
+    main()
+

faiss_index/index.pkl

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d2e4a24d7834bc63e9ae618d90d89e0308c45c69e9db0e475a6582b4b66996e6
+size 2572

requirements.txt

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+aiohttp==3.9.3
+aiosignal==1.3.1
+altair==5.2.0
+annotated-types==0.6.0
+anyio==4.2.0
+attrs==23.2.0
+blinker==1.7.0
+cachetools==5.3.2
+certifi==2024.2.2
+charset-normalizer==3.3.2
+click==8.1.7
+dataclasses-json==0.6.4
+diskcache==5.6.3
+faiss-cpu==1.7.4
+filelock==3.13.1
+frozenlist==1.4.1
+fsspec==2023.12.2
+gitdb==4.0.11
+GitPython==3.1.41
+greenlet==3.0.3
+huggingface-hub==0.20.3
+idna==3.6
+importlib-metadata==7.0.1
+Jinja2==3.1.3
+joblib==1.3.2
+jsonpatch==1.33
+jsonpointer==2.4
+jsonschema==4.21.1
+jsonschema-specifications==2023.12.1
+langchain==0.1.5
+langchain-community==0.0.17
+langchain-core==0.1.18
+langsmith==0.0.86
+llama_cpp_python==0.2.38
+markdown-it-py==3.0.0
+MarkupSafe==2.1.4
+marshmallow==3.20.2
+mdurl==0.1.2
+mpmath==1.3.0
+multidict==6.0.5
+networkx==3.2.1
+nltk==3.8.1
+numpy==1.26.3
+pandas==2.2.0
+pillow==10.2.0
+referencing==0.33.0
+regex==2023.12.25
+requests==2.31.0
+rich==13.7.0
+rpds-py==0.17.1
+safetensors==0.4.2
+scikit-learn==1.4.0
+scipy==1.12.0
+sentence-transformers==2.3.1
+sentencepiece==0.1.99
+smmap==5.0.1
+sniffio==1.3.0
+SQLAlchemy==2.0.25
+streamlit==1.30.0
+sympy==1.12
+tokenizers==0.15.1
+transformers==4.37.2