Delete main.py

main.py

@@ -1,375 +0,0 @@
-from langchain_community.document_loaders import CSVLoader
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-# from langchain_community.embeddings import OpenAIEmbeddings
-from langchain_community.vectorstores import chroma
-from langchain_community.llms import openai
-from langchain.chains import LLMChain
-from dotenv import load_dotenv
-from langchain.chains import ConversationalRetrievalChain
-from langchain_core.prompts import ChatPromptTemplate,PromptTemplate
-from langchain.memory import ConversationBufferMemory
-from langchain_community.chat_models import ChatOpenAI
-from langchain_openai import OpenAIEmbeddings
-from langchain_chroma import Chroma
-import os
-from dotenv import load_dotenv
-import streamlit as st
-import streamlit_chat 
-from langchain_groq import ChatGroq
-global seed 
-from langchain.chains import LLMChain
-from langchain.prompts import PromptTemplate
-from langchain.memory import ConversationBufferMemory
-from langchain_community.chat_models import ChatOpenAI
-from langchain.docstore.document import Document
-from langchain.llms import HuggingFacePipeline
-from langchain.embeddings import HuggingFaceEmbeddings
-
-import pandas as pd
-
-load_dotenv()
-
-
-# OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
-# os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
-
-GROQ_API_KEY = os.getenv("GROQ_API_KEY")
-
-
-class prompts:
-
-    prompt = PromptTemplate.from_template("""
-                                          
-            You are a helpful fitness assistant. Use the following context to answer the question The Level is provided for you to get a better idea on how to answer the question
-                                           .
-            If you don't know the answer, just say that you don't know, don't try to make up an answer.Also make sure to mention the level passed for the user.
-            Context:
-            {context}
-
-            Chat History:
-            {history}
-
-            Question:
-            {question}
-        
-            Level:
-            {level}
-
-            Answer:
-            """)
-
-# Data Filteration
-def filter_transform_data(dataframe):
-
-    dataframe.drop("RatingDesc",axis=1,inplace=True)
-
-    dataframe.dropna(subset=["Desc","Equipment"],inplace=True)
-
-    dataframe.drop("Rating",inplace=True,axis=1)
-
-    # transform data
-
-    document_data = dataframe.to_dict(orient="records")
-
-    return document_data
-
-
-def get_context(vector_store,query,level):
-
-    results = vector_store.max_marginal_relevance_search(
-        
-            query=query,
-            k=5,
-            filter={"Level": level},
-        )
-    
-    # Creating the LLM Chain
-
-        # Pass your context manually from retrieved documents
-    context = "\n\n".join([doc.page_content for doc in results])
-
-    return context
-
-def generate_vector_store():
-
-    # embedding = OpenAIEmbeddings(
-
-    if "vector_store" not in st.session_state:
-
-        langchain_documents = []
-
-        dataframe = pd.read_csv("megaGymDataset.csv",index_col=0)
-
-        document_data = filter_transform_data(dataframe)
-
-        # Iterate through the sample data and create Document objects
-        for item in document_data:
-            # Formulate the page_content string
-            page_content = (
-                f"Title: {item['Title']}\n"
-                f"Type:{item['Type']}\n"
-                f"BodyPart: {item['BodyPart']}\n"
-                f"Desc: {item['Desc']}\n"
-                f"Equipment: {item['Equipment']}\n"
-            )
-            
-            # Create the metadata dictionary
-            metadata = {"Level": item['Level']}
-            
-            # Create the Document object
-            doc = Document(page_content=page_content, metadata=metadata)
-            
-            # Add the Document to our list
-            langchain_documents.append(doc)
-
-        # creating the session_state for vector_store
-
-        # embedding = OpenAIEmbeddings(openai_api_key=os.environ["OPENAI_API_KEY"])
-        embedding = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large-instruct")
-
-        
-        # if path not exist
-        if not os.path.exists("db"):
-
-            st.session_state.vector_store = Chroma.from_documents(langchain_documents,embedding=embedding,collection_name="gym-queries-data",persist_directory = "db")
-            # st.session_state.vector_store.persist()
-
-        else:
-
-            st.session_state.vector_store  = Chroma(
-
-                persist_directory="db",
-                embedding_function=embedding
-            )
-
-    return st.session_state.vector_store
-
-def get_conversational_chain(vector_store,query,level):
-
-    # model_name = "msu-rcc-lair/RuadaptQwen2.5-32B-Instruct"  # Replace with actual name
-
-    # tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
-    # model = AutoModelForCausalLM.from_pretrained(
-    #     model_name,
-    #     device_map="auto",
-    #     torch_dtype=torch.bfloat16,  # or float16
-    #     trust_remote_code=True
-    # )
-    # generator = pipeline(
-    #     "text-generation",
-    #     model=model,
-    #     tokenizer=tokenizer,
-    #     max_new_tokens=512,
-    #     temperature=0.7,
-    #     return_full_text=False
-    # )
-
-
-
-    # llm = HuggingFacePipeline(pipeline=generator)
-    # llm = ChatOpenAI(temperature=0.5,model_name="gpt-4o")
-
-    # llama3-70b-8192
-    llm = ChatGroq(
-            temperature=1, 
-            groq_api_key = GROQ_API_KEY, 
-            model_name="llama-3.1-8b-instant",
-            max_tokens=560,
-        #     top_p=0.95,
-        #     frequency_penalty=1,
-        #     presence_penalty=1,
-    )
-    # llm_chain = LLMChain(llm=llm, prompt=prompts.prompt)
-
-    if "memory" not in st.session_state:
-
-        st.session_state.memory = ConversationBufferMemory(memory_key="history", input_key="question", return_messages=True)
-
-        st.session_state.conversational_chain = LLMChain(
-            llm=llm,
-            # taking the prompt template
-            prompt=prompts.prompt,
-            memory=st.session_state.memory
-        )
-
-
-    return st.session_state.conversational_chain,st.session_state.memory
-
-def stick_it_good():
-
-    # make header sticky.
-    st.markdown(
-        """
-            <div class='fixed-header'/>
-            <style>
-                div[data-testid="stVerticalBlock"] div:has(div.fixed-header) {
-                    position: sticky;
-                    top: 2.875rem;
-                    background-color: ##393939;
-                    z-index: 999;
-                }
-                .fixed-header {
-                    border-bottom: 1px solid black;
-                }
-            </style>
-        """,
-        unsafe_allow_html=True
-    )
-
-
-def show_privacy_policy():
-    st.title("Privacy Policy")
-   
-
-def show_terms_of_service():
-    st.title("Terms of Service")
-   
-seed = 0
-
-def main():
-
-    global seed
-
-    page = st.sidebar.selectbox("Choose a page", ["Home", "Privacy Policy", "Terms of Service"])
-
-    if page == "Privacy Policy":
-
-        show_privacy_policy()
-
-    elif page == "Terms of Service":
-
-        show_terms_of_service()
-
-    else:
-
-        st.write("Welcome to the Home Page")
-
-        with st.container():
-
-            st.title("Workout Wizard")
-            stick_it_good()
-            
-
-        with st.sidebar:
-
-            if "seed" not in st.session_state:
-                
-                st.session_state.seed = 0
-                
-            # Display the image using the URL
-
-            choose_mode = st.selectbox('Choose Workout Level',["Beginner","Intermediate","Expert"])
-
-
-            st.markdown("<h2 style='text-align: center;'>Choose Your Avatar</h2>", unsafe_allow_html=True)
-
-            # st.markdown(f"<h2 style='text-align: center;'>{st.button("Back")}</h2>", unsafe_allow_html=True)
-
-            # Center the buttons using HTML and CSS
-            col1, col2, col3 = st.columns([1, 1, 1])
-
-    
-            with col1:
-                
-                st.write("")  # Empty column for spacing
-
-            with col2:
-
-                print(st.session_state.seed)
-
-                choose_Avatar = st.button("Next")
-
-                choose_Avatar_second = st.button("Back")
-
-
-                if choose_Avatar:
-
-                    st.session_state.seed += 1
-
-                if choose_Avatar_second:
-
-                    st.session_state.seed -= 1
-
-                avatar_url = f"https://api.dicebear.com/9.x/adventurer/svg?seed={st.session_state.seed}"
-
-                st.image(avatar_url, caption=f"Avatar {st.session_state.seed }")
-
-            with col3:
-
-                st.write("")  # Empty column for spacing
-
-        
-        streamlit_chat.message("Hi. I'm your friendly Gym Assistant Bot.")
-        streamlit_chat.message("Ask me anything about the gym! Just don’t ask me to do any push-ups... I'm already *up* and running!")
-        streamlit_chat.message("If you want to change your workout level and avatar, press the top left arrow and you will have options to make changes")
-
-
-        question = st.chat_input("Ask a question related to your GYM queries")
-
-
-        if "conversation_chain" not in st.session_state:
-
-            st.session_state.conversation_chain = None
-
-
-        # if question:
-
-        # Converstion chain
-        if st.session_state.conversation_chain == None:
-            # st.session_state.vectors
-
-            print("the vector store generated")
-
-            st.session_state.vector_store = generate_vector_store()
-
-            st.session_state.conversation_chain, st.session_state.memory = get_conversational_chain(st.session_state.vector_store,question,choose_mode)
-
-        # the session state memory
-        if st.session_state.memory != None:
-
-            for i,message in enumerate(st.session_state.memory.chat_memory.messages):
-
-                if i%2 == 0:
-
-                    suffix = f" for {choose_mode} level"
-
-                    # Check if the message ends with the suffix and strip it
-                    if message.content.endswith(suffix):
-
-                        message.content = message.content[:-len(suffix)]
-
-                    # message.content = message.content.strip(f" for {choose_mode} level")
-
-                    print("this is the message content",message.content)
-
-                    streamlit_chat.message(message.content,is_user=True, avatar_style="adventurer",seed=st.session_state.seed, key=f"user_msg_{i}")
-
-                else:
-
-                    streamlit_chat.message(message.content,key=f"bot_msg_{i}")
-
-                    st.write("--------------------------------------------------")
-
-        if question:
-            
-                streamlit_chat.message(question,is_user=True, avatar_style="adventurer",seed=st.session_state.seed)
-
-                print(question)
-
-                print("------------------------")
-
-                # GETTING THE CONTEXT AND ANSWER FROM THE MODEL
-
-                context = get_context(st.session_state.vector_store,question,choose_mode)
-
-                print("context::",context)
-                print("the choose mode:",choose_mode)
-
-                response = st.session_state.conversational_chain.run({"context": context, "question": question,"level":choose_mode})
-
-                streamlit_chat.message(response)
-
-        
-if __name__ == "__main__":
-
-    main()