LangChain based Chatbot added! (#28)

Generative AI/Langchain based Chat Retrival Chatbot/README.md

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+---
+title: ! 
+emoji: 🏠
+colorFrom: #FF5733
+colorTo: #FFBF00
+sdk: streamlit
+sdk_version: 1.25.0
+app_file: app.py
+pinned: false
+---
+
+
+Dataset Link : https://www.kaggle.com/datasets/sameep98/housing-prices-in-mumbai

Generative AI/Langchain based Chat Retrival Chatbot/app.py

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+import streamlit as st
+from langchain.chains import ConversationalRetrievalChain
+from langchain.document_loaders import CSVLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.llms import CTransformers
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.document_loaders.csv_loader import CSVLoader
+
+# Set Streamlit page configuration
+st.set_page_config(
+    page_title="ApnaGhar 🏠 - Mumbai Property Insights",
+    page_icon="",  # You can set an icon if needed
+    layout="wide",
+    initial_sidebar_state="expanded",
+)
+
+# Load data and set up language chain components
+loader = CSVLoader(file_path='Mumbai1.csv')
+documents = loader.load()
+
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=50)
+text_chunks = text_splitter.split_documents(documents)
+
+embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2",
+                                   model_kwargs={'device': "cpu"})
+
+vector_store = FAISS.from_documents(text_chunks, embeddings)
+
+llm = CTransformers(model="link llma model", model_type="llama",
+                    config={'max_new_tokens': 128, 'temperature': 0.01})
+
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+
+chain = ConversationalRetrievalChain.from_llm(llm=llm, chain_type='stuff',
+                                              retriever=vector_store.as_retriever(search_kwargs={"k": 2}),
+                                              memory=memory)
+
+st.sidebar.title("ApnaGhar 🏠 - Mumbai Property Insights")
+st.sidebar.info("Welcome to ApnaGhar! Explore Mumbai's real estate and neighborhood information.")
+github_link = "[GitHub]()"
+st.sidebar.info("To contribute and Sponsor - " + github_link)
+
+if 'query_stack' not in st.session_state:
+    st.session_state['query_stack'] = []
+
+st.sidebar.subheader("Property Queries:")
+for i, past_query in enumerate(reversed(st.session_state['query_stack'])):
+    st.sidebar.write(f"{i + 1}. {past_query}")
+
+st.title("ApnaGhar - Mumbai Property Insights")
+
+if 'history' not in st.session_state:
+    st.session_state['history'] = []
+
+if 'generated' not in st.session_state:
+    st.session_state['generated'] = ["Hello! I'm ApnaGhar, here to help you with Mumbai property insights."]
+
+if 'past' not in st.session_state:
+    st.session_state['past'] = ["Hello!"]
+
+reply_container = st.container()
+container = st.container()
+
+with container:
+    with st.form(key='user_form', clear_on_submit=True):
+        user_input = st.text_input("Your Query:", placeholder="Ask anything about Mumbai properties or neighborhoods", key='input_user')
+        submit_button_user = st.form_submit_button(label='Send')
+
+    try:
+        if submit_button_user and user_input:
+            output = chain({"question": user_input, "chat_history": st.session_state['history']})["answer"]
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
+
+            # Push user input to the stack
+            st.session_state['query_stack'].append(user_input)
+    except Exception as e:
+        st.error(f"An error occurred: {str(e)}")
+
+if st.session_state['generated']:
+    with reply_container:
+        for i in range(len(st.session_state['generated'])):
+            user_message = st.session_state["past"][i]
+            generated_message = st.session_state["generated"][i]
+
+            st.text(f"User: {user_message}")
+
+            st.text(f"ApnaGhar: {generated_message}")

Generative AI/Langchain based Chat Retrival Chatbot/requirements.txt

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+langchain
+torch
+accelerate
+#bitsandbytes
+transformers
+sentence_transformers
+streamlit
+streamlit_chat
+faiss-cpu
+altair
+tiktoken
+huggingface-hub
+pypdf
+ctransformers