Upload main.py

main.py

@@ -0,0 +1,376 @@
+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()