Create app.py

app.py

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+# Import necessary libraries
+import streamlit as st
+from streamlit_chat import message
+import tempfile
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.llms import CTransformers
+from langchain.chains import ConversationalRetrievalChain
+
+import sys
+sys.path.append(r"vectorstore/db_faiss")
+import dataset_utils
+
+# Define the path for generated embeddings
+DB_FAISS_PATH = 'vectorstore/db_faiss'
+
+# Load the model of choice
+def load_llm():
+    llm = CTransformers(
+        model="llama-2-7b-chat.ggmlv3.q8_0.bin",
+        model_type="llama",
+        max_new_tokens=512,
+        temperature=0.5
+    )
+    return llm
+
+# Set the title for the Streamlit app
+st.title("Llama2 Chat CSV - 🦜🦙")
+
+# Create a file uploader in the sidebar
+uploaded_file = st.sidebar.file_uploader("Upload File", type="csv")
+
+# Handle file upload
+if uploaded_file:
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
+
+    # Load CSV data using CSVLoader
+    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={'delimiter': ','})
+    data = loader.load()
+
+    # Create embeddings using Sentence Transformers
+    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2', model_kwargs={'device': 'cpu'})
+
+    # Create a FAISS vector store and save embeddings
+    db = FAISS.from_documents(data, embeddings)
+    db.save_local(DB_FAISS_PATH)
+
+    # Load the language model
+    llm = load_llm()
+
+    # Create a conversational chain
+    chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
+
+    # Function for conversational chat
+    def conversational_chat(query):
+        result = chain({"question": query, "chat_history": st.session_state['history']})
+        st.session_state['history'].append((query, result["answer"]))
+        return result["answer"]
+
+    # Initialize chat history
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+
+    # Initialize messages
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ["Hello ! Ask me(LLAMA2) about " + uploaded_file.name + " 🤗"]
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey ! 👋"]
+
+    # Create containers for chat history and user input
+    response_container = st.container()
+    container = st.container()
+
+    # User input form
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input("Query:", placeholder="Talk to csv data 👉 (:", key='input')
+            submit_button = st.form_submit_button(label='Send')
+
+        if submit_button and user_input:
+            output = conversational_chat(user_input)
+            st.session_state['past'].append(user_input)
+            st.session_state['generated'].append(output)
+
+    # Display chat history
+    if st.session_state['generated']:
+        with response_container:
+            for i in range(len(st.session_state['generated'])):
+                message(st.session_state["past"][i], is_user=True, key=str(i) + '_user', avatar_style="big-smile")
+                message(st.session_state["generated"][i], key=str(i), avatar_style="thumbs")