Spaces:
Sleeping
Sleeping
File size: 3,958 Bytes
0b1eb85 2cf8f22 0b1eb85 2cf8f22 0b1eb85 2cf8f22 0b1eb85 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 |
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader
from llama_index import download_loader
from pandasai.llm.openai import OpenAI
from matplotlib import pyplot as plt
import streamlit as st
import pandas as pd
import os
documents_folder = "./documents"
# Load PandasAI loader, Which is a wrapper over PandasAI library
PandasAIReader = download_loader("PandasAIReader")
st.title("Welcome to `ChatwithDocs`")
st.header("Interact with Documents such as `PDFs/CSV/Docs` using the power of LLMs\nPowered by `LlamaIndex🦙` \nCheckout the [GITHUB Repo Here](https://github.com/anoopshrma/Chat-with-Docs) and Leave a star⭐")
def get_csv_result(df, query):
reader = PandasAIReader(llm=csv_llm)
response = reader.run_pandas_ai(
df,
query,
is_conversational_answer=False
)
return response
def save_file(doc):
fn = os.path.basename(doc.name)
# open read and write the file into the server
open(documents_folder+'/'+fn, 'wb').write(doc.read())
# Check for the current filename, If new filename
# clear the previous cached vectors and update the filename
# with current name
if st.session_state.get('file_name'):
if st.session_state.file_name != fn:
st.cache_resource.clear()
st.session_state['file_name'] = fn
else:
st.session_state['file_name'] = fn
return fn
def remove_file(file_path):
# Remove the file from the Document folder once
# vectors are created
if os.path.isfile(documents_folder+'/'+file_path):
os.remove(documents_folder+'/'+file_path)
@st.cache_resource
def create_index():
# Create vectors for the file stored under Document folder.
# NOTE: You can create vectors for multiple files at once.
documents = SimpleDirectoryReader(documents_folder).load_data()
index = GPTVectorStoreIndex.from_documents(documents)
return index
def query_doc(vector_index, query):
# Applies Similarity Algo, Finds the nearest match and
# take the match and user query to OpenAI for rich response
query_engine = vector_index.as_query_engine()
response = query_engine.query(query)
return response
api_key = st.text_input("Enter your OpenAI API key here:", type="password")
if api_key:
os.environ['OPENAI_API_KEY'] = api_key
csv_llm = OpenAI(api_token=api_key)
tab1, tab2= st.tabs(["CSV", "PDFs/Docs"])
with tab1:
st.write("Chat with CSV files using PandasAI loader with LlamaIndex")
input_csv = st.file_uploader("Upload your CSV file", type=['csv'])
if input_csv is not None:
st.info("CSV Uploaded Successfully")
df = pd.read_csv(input_csv)
st.dataframe(df, use_container_width=True)
st.write("---")
input_text = st.text_area("Ask your query")
if input_text is not None:
if st.button("Send"):
st.info("Your query: "+ input_text)
with st.spinner('Processing your query...'):
response = get_csv_result(df, input_text)
if plt.get_fignums():
st.pyplot(plt.gcf())
else:
st.success(response)
with tab2:
st.write("Chat with PDFs/Docs")
input_doc = st.file_uploader("Upload your Docs")
if input_doc is not None:
st.info("Doc Uploaded Successfully")
file_name = save_file(input_doc)
index = create_index()
remove_file(file_name)
st.write("---")
input_text = st.text_area("Ask your question")
if input_text is not None:
if st.button("Ask"):
st.info("Your query: \n" +input_text)
with st.spinner("Processing your query.."):
response = query_doc(index, input_text)
print(response)
st.success(response)
st.write("---")
# Shows the source documents context which
# has been used to prepare the response
st.write("Source Documents")
st.write(response.get_formatted_sources()) |