first commit

app.py

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+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 langchian.llms import CTransformers
+from langchain.chains import ConversationalRetrievalChain
+
+DB_FAISS_PATH ='vectorstore/db_faiss'
+
+#Loading the model
+def load_llm():
+    llm = CTransformers(
+        model= "TheBloke/Llama-2-7B-Chat-GGML",
+        max_new_tokens=512,
+        temperature=0.5
+    )
+    return llm
+
+st.image("https://huggingface.co/spaces/wiwaaw/summary/resolve/main/banner.png")
+st.title("Chat with CSV using Llama2")
+
+uploaded_file = st.sidebar.file_uploader("Upload your data", type="csv")
+if uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
+        tmp_file.write(uploaded_file.getvalue())
+        tmp_file_path = tmp_file.name
+    
+    loader = CSVLoader(file_path=tmp_file_path, encoding="utf-8", csv_args={
+        'delimiter': ',',  # default value
+    })
+    data = loader.load()
+    embeddings=HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2',
+                                     model_kwargs={'device': 'cpu'})
+    db = FAISS.from_documents(data, embeddings)
+    db.save_local(DB_FAISS_PATH)
+    llm = load_llm()
+    chain=ConversationalRetrievalChain.from_llm(llm=llm, retriever=db.as_retriever())
+
+    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']
+    
+    if 'history' not in st.session_state:
+        st.session_state['history'] = []
+        
+    if 'generated' not in st.session_state:
+        st.session_state['generated'] = ''
+
+    if 'past' not in st.session_state:
+        st.session_state['past'] = ["Hey!"]
+    
+    #container for the chat history
+    response_container = st.container()
+    #container for the user's text input 
+    container = st.container()
+
+    with container:
+        with st.form(key='my_form', clear_on_submit=True):
+            user_input = st.text_input('Query:', placeholder="Talk to your csv data here:", 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)
+    
+    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")

requirements.txt

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+streamlit
+streamlit-chat
+pypdf
+langchain
+torch
+accelerate
+bitsandbytes
+transformers
+sentence_transformers
+faiss_cpu
+ctransformers