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model.py ADDED
@@ -0,0 +1,81 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
1
+ import streamlit as st
2
+ import os
3
+ from langchain.document_loaders.csv_loader import CSVLoader
4
+ from langchain.text_splitter import RecursiveCharacterTextSplitter
5
+ from langchain.embeddings import HuggingFaceEmbeddings
6
+ from langchain.vectorstores import FAISS
7
+ from langchain.llms import CTransformers
8
+ from langchain.chains import ConversationalRetrievalChain
9
+
10
+ def add_vertical_space(spaces=1):
11
+ for _ in range(spaces):
12
+ st.sidebar.markdown("---")
13
+
14
+ def main():
15
+ st.set_page_config(page_title="Llama-2-GGML CSV Chatbot", layout="wide")
16
+ st.title("Llama-2-GGML CSV Chatbot")
17
+
18
+ st.sidebar.title("About")
19
+ st.sidebar.markdown('''
20
+ The Llama-2-GGML CSV Chatbot uses the **Llama-2-7B-Chat-GGML** model.
21
+
22
+ ### πŸ”„Bot evolving, stay tuned!
23
+
24
+ ## Useful Links πŸ”—
25
+
26
+ - **Model:** [Llama-2-7B-Chat-GGML](https://huggingface.co/TheBloke/Llama-2-7B-Chat-GGML/tree/main) πŸ“š
27
+ - **GitHub:** [ThisIs-Developer/Llama-2-GGML-CSV-Chatbot](https://github.com/ThisIs-Developer/Llama-2-GGML-CSV-Chatbot) πŸ’¬
28
+ ''')
29
+
30
+ DB_FAISS_PATH = "vectorstore/db_faiss"
31
+ TEMP_DIR = "temp"
32
+
33
+ if not os.path.exists(TEMP_DIR):
34
+ os.makedirs(TEMP_DIR)
35
+
36
+ uploaded_file = st.sidebar.file_uploader("Upload CSV file", type=['csv'], help="Upload a CSV file")
37
+
38
+ add_vertical_space(1)
39
+ st.sidebar.markdown('Made by [@ThisIs-Developer](https://huggingface.co/ThisIs-Developer)')
40
+
41
+ if uploaded_file is not None:
42
+ file_path = os.path.join(TEMP_DIR, uploaded_file.name)
43
+ with open(file_path, "wb") as f:
44
+ f.write(uploaded_file.getvalue())
45
+
46
+ st.write(f"Uploaded file: {uploaded_file.name}")
47
+ st.write("Processing CSV file...")
48
+
49
+ loader = CSVLoader(file_path=file_path, encoding="utf-8", csv_args={'delimiter': ','})
50
+ data = loader.load()
51
+
52
+ text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=20)
53
+ text_chunks = text_splitter.split_documents(data)
54
+
55
+ st.write(f"Total text chunks: {len(text_chunks)}")
56
+
57
+ embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-MiniLM-L6-v2')
58
+ docsearch = FAISS.from_documents(text_chunks, embeddings)
59
+ docsearch.save_local(DB_FAISS_PATH)
60
+
61
+ llm = CTransformers(model="models/llama-2-7b-chat.ggmlv3.q4_0.bin",
62
+ model_type="llama",
63
+ max_new_tokens=512,
64
+ temperature=0.1)
65
+
66
+ qa = ConversationalRetrievalChain.from_llm(llm, retriever=docsearch.as_retriever())
67
+
68
+ st.write("### Enter your query:")
69
+ query = st.text_input("Input Prompt:")
70
+ if query:
71
+ with st.spinner("Processing your question..."):
72
+ chat_history = []
73
+ result = qa({"question": query, "chat_history": chat_history})
74
+ st.write("---")
75
+ st.write("### Response:")
76
+ st.write(f"> {result['answer']}")
77
+
78
+ os.remove(file_path)
79
+
80
+ if __name__ == "__main__":
81
+ main()
models/llama-2-7b-chat.ggmlv3.q4_0.bin ADDED
@@ -0,0 +1,3 @@
 
 
 
 
1
+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:8daa9615cce30c259a9555b1cc250d461d1bc69980a274b44d7eda0be78076d8
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+ size 3791725184
requirements.txt ADDED
@@ -0,0 +1,4 @@
 
 
 
 
 
1
+ langchain
2
+ ctransformers
3
+ sentence-transformers
4
+ faiss-cpu
vectorstore/db_faiss/index.faiss ADDED
Binary file (240 kB). View file
 
vectorstore/db_faiss/index.pkl ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ version https://git-lfs.github.com/spec/v1
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+ oid sha256:d65f4a88b66d30f860ff0747717b482b0e0378638a19fbcc8cb003b5b434aa44
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+ size 52346