drkareemkamal commited on
Commit
6903cc5
·
verified ·
1 Parent(s): 7a3a8ae

Create app.py

Browse files
Files changed (1) hide show
  1. app.py +109 -0
app.py ADDED
@@ -0,0 +1,109 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import os
3
+ from streamlit_chat import message
4
+ import tempfile
5
+ #from langchain_community.documentloader.csv_loader import CSVLoader
6
+ from langchain_community.document_loaders.csv_loader import CSVLoader
7
+ from langchain_community.embeddings import HuggingFaceEmbeddings
8
+ #from langchain_community.embeddings import HuggingFaceBgeEmbeddings
9
+ from langchain.text_splitter import RecursiveCharacterTextSplitter
10
+ from langchain_community.vectorstores import FAISS
11
+ #from langchain_community.llms import CTransformers
12
+ from langchain_community.llms.ctransformers import CTransformers
13
+
14
+ from langchain.chains.conversational_retrieval.base import ConversationalRetrievalChain
15
+
16
+ #from langchain.chains.conversational_retrieval.base import ConversationalRetreievalChain
17
+
18
+
19
+ DB_FAISS_PATH = 'vectorstore/db_faiss'
20
+ TEMP_DIR = 'temp'
21
+ if not os.path.exists(TEMP_DIR):
22
+ os.makedirs(TEMP_DIR)
23
+
24
+ def load_llm():
25
+ # load model from hugging face repo
26
+ llm = CTransformers(
27
+ model = 'TheBloke/Llama-2-7B-Chat-GGML',
28
+ model_type = 'llama',
29
+ max_new_token = 512,
30
+ temperature = 0.5
31
+ )
32
+ return llm
33
+
34
+ st.title("Chat with CSV using Llma 2")
35
+ st.markdown("<h1 style='text-align: center; color: blue;'>Chat with your PDF 📄 </h1>", unsafe_allow_html=True)
36
+ st.markdown("<h3 style='text-align: center; color: grey;'>Built by <a href='https://github.com/DrKareemKAmal'>MindSparks ❤️ </a></h3>", unsafe_allow_html=True)
37
+
38
+ uploaded_file = st.sidebar.file_uploader('Upload your data', type=['csv'])
39
+
40
+ if uploaded_file:
41
+ # with tempfile.NamedTemporaryFile(delete=False)as temp_file :
42
+ # temp_file.write(uploaded_file.getvalue())
43
+ # tempfile_path = temp_file.name
44
+ file_path = os.path.join(TEMP_DIR, uploaded_file.name)
45
+ with open(file_path, "wb") as f:
46
+ f.write(uploaded_file.getvalue())
47
+
48
+ st.write(f"Uploaded file: {uploaded_file.name}")
49
+ st.write("Processing CSV file...")
50
+
51
+ loader = CSVLoader(file_path = file_path, encoding = 'utf-8',
52
+ csv_args = {'delimiter': ','} )
53
+ data = loader.load()
54
+ #st.json(data)
55
+
56
+ text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500 , chunk_overlap = 20)
57
+ text_chunks = text_splitter.split_documents(data)
58
+ st.write (f"Total text chunks : {len(text_chunks)}")
59
+
60
+ embeddings = HuggingFaceEmbeddings(
61
+ model_name = 'sentence-transformers/all-MiniLM-L6-v2',
62
+ # model_kwargs = {'device': 'cpu'}
63
+ )
64
+
65
+
66
+ db = FAISS.from_documents(text_chunks, embeddings)
67
+ db.save_local (DB_FAISS_PATH)
68
+ llm = load_llm()
69
+
70
+ chain = ConversationalRetrievalChain.from_llm(llm= llm , retriever = db.as_retriever())
71
+
72
+ def conversational_chat(query):
73
+ result = chain({"question": query ,
74
+ "chat_history": st.session_state['history']})
75
+ st.session_state['history'].append((query , result['answer']))
76
+ return result['answer']
77
+
78
+ if 'history' not in st.session_state :
79
+ st.session_state['history'] = []
80
+
81
+ if 'generated' not in st.session_state :
82
+ st.session_state['generated'] = ['Hello, Ask me anything about ' + uploaded_file.name]
83
+
84
+ if 'past' not in st.session_state :
85
+ st.session_state['past'] = ['Hey !']
86
+
87
+ # Container for the chat history
88
+ response_container = st.container()
89
+ container = st.container()
90
+
91
+ with container :
92
+ with st.form(key = 'my_form',
93
+ clear_on_submit=True):
94
+ user_input = st.text_input('Query:', placeholder= "Talk to youur CSV Data here ")
95
+ submit_button = st.form_submit_button(label = 'chat')
96
+
97
+ if submit_button and user_input :
98
+ output = conversational_chat(user_input)
99
+
100
+ st.session_state['past'].append(user_input)
101
+ st.session_state['generated'].append(output)
102
+
103
+ if st.session_state['generated'] :
104
+ with response_container:
105
+ for i in range(len(st.session_state['generated'])):
106
+ message(st.session_state['past'][i], is_user = True , key=str(i) + '_user',
107
+ avatar_style='big-smile')
108
+ message(st.session_state['generated'][i], key = str(i), avatar_style='thumb')
109
+