File size: 4,809 Bytes
d7bf121 |
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 |
import streamlit as st
import pandas as pd
import plotly.express as px
from pandasai import Agent
from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI
from langchain.chains import RetrievalQA
from langchain.schema import Document
from datasets import load_dataset
import os
# Title
st.title("PandasAI Data Analysis Tool with RAG")
# Fetch API keys from environment variables
api_key = os.getenv("OPENAI_API_KEY")
pandasai_api_key = os.getenv("PANDASAI_API_KEY")
# Dataset selection
st.sidebar.header("Dataset Input Options")
input_option = st.sidebar.radio("Select Dataset Input:", ["Use Hugging Face Dataset", "Upload CSV File"])
# Initialize session state for the dataframe
if "df" not in st.session_state:
st.session_state.df = None
# Dataset loading logic
if input_option == "Use Hugging Face Dataset":
dataset_name = st.sidebar.text_input("Enter Hugging Face Dataset Name:", value="HUPD/hupd")
if st.sidebar.button("Load Dataset"):
try:
dataset = load_dataset(dataset_name, name="sample", split="train", trust_remote_code=True)
st.session_state.df = pd.DataFrame(dataset)
st.sidebar.success(f"Dataset '{dataset_name}' loaded successfully!")
except Exception as e:
st.sidebar.error(f"Error loading dataset: {e}")
elif input_option == "Upload CSV File":
uploaded_file = st.sidebar.file_uploader("Upload CSV File:", type=["csv"])
if uploaded_file:
try:
st.session_state.df = pd.read_csv(uploaded_file)
st.sidebar.success("File uploaded successfully!")
except Exception as e:
st.sidebar.error(f"Error loading file: {e}")
# Show the loaded dataframe preview
if st.session_state.df is not None:
st.subheader("Dataset Preview")
st.dataframe(st.session_state.df.head(10))
# Set up PandasAI Agent
agent = Agent(st.session_state.df)
# Convert DataFrame to documents
documents = [
Document(
page_content=", ".join([f"{col}: {row[col]}" for col in st.session_state.df.columns]),
metadata={"index": index}
)
for index, row in st.session_state.df.iterrows()
]
# Set up RAG
embeddings = OpenAIEmbeddings()
vectorstore = FAISS.from_documents(documents, embeddings)
retriever = vectorstore.as_retriever()
qa_chain = RetrievalQA.from_chain_type(
llm=ChatOpenAI(),
chain_type="stuff",
retriever=retriever
)
# Create tabs for different functionality
tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"])
with tab1:
st.header("Data Analysis with PandasAI")
pandas_question = st.text_input("Ask a question about your data (PandasAI):")
if pandas_question:
result = agent.chat(pandas_question)
st.write("PandasAI Answer:", result)
with tab2:
st.header("Q&A with RAG")
rag_question = st.text_input("Ask a question about your data (RAG):")
if rag_question:
result = qa_chain.run(rag_question)
st.write("RAG Answer:", result)
with tab3:
st.header("Data Visualization")
viz_question = st.text_input("What kind of graph would you like to see? (e.g., 'Show a scatter plot of salary vs experience')")
if viz_question:
try:
result = agent.chat(viz_question)
# Convert the PandasAI result into executable code
import re
code_pattern = r'```python\n(.*?)\n```'
code_match = re.search(code_pattern, result, re.DOTALL)
if code_match:
viz_code = code_match.group(1)
# Modify the code to use 'px' instead of 'plt'
viz_code = viz_code.replace('plt.', 'px.')
viz_code = viz_code.replace('plt.show()', 'fig = px.scatter(df, x=x, y=y)')
# Execute the code and display the graph
exec(viz_code)
st.plotly_chart(fig)
else:
st.write("Failed to generate a graph. Please try asking differently.")
except Exception as e:
st.write(f"An error occurred: {str(e)}")
st.write("Please try rephrasing your question.")
else:
st.warning("No dataset loaded. Please select a dataset input option from the sidebar.")
# Error handling for missing API keys
if not api_key:
st.error("Missing OpenAI API Key in environment variables.")
if not pandasai_api_key:
st.error("Missing PandasAI API Key in environment variables.") |