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import streamlit as st
import pandas as pd
import plotly.express as px
from pandasai import Agent
from pandasai.llm.openai import OpenAI
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
import os
import logging
# Configure logging
logging.basicConfig(level=logging.DEBUG)
logger = logging.getLogger(__name__)
# Set the title of the app
st.title("Data Analyzer on Hugging Face Spaces")
# Fetch API keys from environment variables
api_key = os.getenv("OPENAI_API_KEY")
pandasai_api_key = os.getenv("PANDASAI_API_KEY")
if not api_key or not pandasai_api_key:
st.error(
"API keys not found in the environment. Please set the 'OPENAI_API_KEY' and 'PANDASAI_API_KEY' environment variables."
)
logger.error("API keys not found. Ensure they are set in the environment variables.")
else:
# File uploader
uploaded_file = st.file_uploader("Upload an Excel or CSV file", type=["xlsx", "csv"])
if uploaded_file is not None:
try:
# Load the data
if uploaded_file.name.endswith('.xlsx'):
df = pd.read_excel(uploaded_file)
else:
df = pd.read_csv(uploaded_file)
st.write("Data Preview:")
st.write(df.head())
logger.info(f"Uploaded file loaded successfully with shape: {df.shape}")
# Initialize PandasAI Agent
llm = OpenAI(api_key=pandasai_api_key, max_tokens=1500, timeout=60)
agent = Agent(df, llm=llm)
# Convert the DataFrame into documents for RAG
documents = [
Document(
page_content=", ".join([f"{col}: {row[col]}" for col in df.columns if pd.notnull(row[col])]),
metadata={"index": index}
)
for index, row in df.iterrows()
]
logger.info(f"{len(documents)} documents created for RAG.")
# 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
tab1, tab2, tab3 = st.tabs(["PandasAI Analysis", "RAG Q&A", "Data Visualization"])
# Tab 1: PandasAI Analysis
with tab1:
st.header("Data Analysis using PandasAI")
pandas_question = st.text_input("Ask a question about the data (PandasAI):")
if pandas_question:
try:
result = agent.chat(pandas_question)
if result:
st.write("PandasAI Answer:", result)
else:
st.warning("PandasAI returned no result. Please try another question.")
except Exception as e:
st.error(f"Error from PandasAI: {e}")
logger.error(f"PandasAI error: {e}")
# Tab 2: RAG Q&A
with tab2:
st.header("Question Answering using RAG")
rag_question = st.text_input("Ask a question about the data (RAG):")
if rag_question:
try:
result = qa_chain.run(rag_question)
st.write("RAG Answer:", result)
except Exception as e:
st.error(f"Error from RAG Q&A: {e}")
logger.error(f"RAG error: {e}")
# Tab 3: Data Visualization
with tab3:
st.header("Data Visualization")
viz_question = st.text_input("What kind of graph would you like to create? (e.g., 'Show a scatter plot of salary vs experience')")
if viz_question:
try:
result = agent.chat(viz_question)
# Since PandasAI output is text, extract 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)
logger.debug(f"Extracted visualization code: {viz_code}")
# Modify code to use Plotly (px) instead of matplotlib (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 chart
exec(viz_code)
st.plotly_chart(fig)
else:
st.warning("Unable to generate a graph. Please try a different query.")
logger.warning("No valid visualization code found in PandasAI response.")
except Exception as e:
st.error(f"An error occurred: {e}")
logger.error(f"Visualization error: {e}")
except Exception as e:
st.error(f"An error occurred while processing the file: {e}")
logger.error(f"File processing error: {e}")
else:
st.info("Please upload a file to begin analysis.")
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