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| 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 `RegBot5`") | |
| 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) | |
| 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()) |