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import streamlit as st
import pandas as pd
import os
import sqlite3
from langchain_community.utilities.sql_database import SQLDatabase
from langchain.chains import create_sql_query_chain
from langchain_openai import AzureChatOpenAI
from langchain_community.tools.sql_database.tool import QuerySQLDataBaseTool
from operator import itemgetter
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePassthrough
from ydata_profiling import ProfileReport
import streamlit.components.v1 as components
import tempfile
from langchain_openai import ChatOpenAI
# Enhanced Page Configuration
st.set_page_config(
page_title="Chat with Excel/CSV",
page_icon=":bar_chart:",
layout="centered",
initial_sidebar_state="expanded"
)
# Custom CSS for styling
st.markdown(
"""
<style>
/* Main Layout */
.main {background-color: white;}
/* Sidebar Styling */
.sidebar .sidebar-content {
background-color: #F1F5F9;
color: black;
}
.sidebar .sidebar-content .stButton>button, .sidebar .sidebar-content h1, .sidebar .sidebar-content h2 {
color: #1A202C;
}
/* Gradient Text for Main Greeting */
.greeting-text {
font-size: 3em;
color: transparent;
background-image: linear-gradient(90deg, #3b82f6, #ec4899);
-webkit-background-clip: text;
font-weight: 600;
text-align: center;
}
/* Chat Input Styling */
.stTextInput > div > input {
background-color: #F1F5F9;
color: #1A202C;
border-radius: 8px;
padding: 10px;
margin-top: 10px;
width: 100%;
}
/* Button Styling */
.stButton > button {
background-color: #3b82f6;
color: white;
border: none;
border-radius: 5px;
padding: 0.5em 1em;
font-size: 1em;
font-weight: 600;
}
</style>
""",
unsafe_allow_html=True
)
# Function to handle Q&A option
def code_for_option_1(api_key):
st.write('<div class="greeting-text">Hello, Sangram!</div>', unsafe_allow_html=True)
st.sidebar.info("Ask any question about the uploaded Excel or CSV data.")
st.sidebar.image("https://miro.medium.com/v2/resize:fit:786/format:webp/1*qUFgGhSERoWAa08MV6AVCQ.jpeg", use_container_width=True)
uploaded_file = st.file_uploader("Upload Excel or CSV file:", type=["xlsx", "csv"])
if uploaded_file is not None:
# Use temporary file for uploaded content
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_file_path = tmp_file.name
# Load Excel or CSV file
if uploaded_file.name.endswith(".xlsx"):
df = pd.read_excel(tmp_file_path)
elif uploaded_file.name.endswith(".csv"):
df = pd.read_csv(tmp_file_path)
st.write("### Uploaded Data:")
st.dataframe(df.head(len(df)))
question = st.text_input("Ask a question:")
submit = st.button("Ask")
if submit:
st.subheader("Answer:")
st.write("Please wait, answer is generating...")
# Initialize OpenAI chat model using the provided API key
llm_1 = ChatOpenAI(model="gpt-3.5-turbo", temperature=0, openai_api_key=api_key)
with sqlite3.connect(f"{uploaded_file.name}.db") as conn:
df.to_sql(f"{uploaded_file.name}s", conn, if_exists="replace")
db = SQLDatabase.from_uri(f"sqlite:///{uploaded_file.name}.db")
generate_query = create_sql_query_chain(llm_1, db)
execute_query = QuerySQLDataBaseTool(db=db)
answer_prompt = PromptTemplate.from_template(
"""Given the following user question, SQL query, and SQL result, answer the question.
Question: {question}
SQL Query: {query}
SQL Result: {result}
Answer: """
)
rephrase_answer = answer_prompt | llm_1 | StrOutputParser()
chain = (
RunnablePassthrough.assign(query=generate_query)
.assign(result=itemgetter("query") | execute_query)
| rephrase_answer
)
response = chain.invoke({"question": question})
st.subheader(response)
# Function to handle EDA option
def code_for_option_2():
st.sidebar.image("https://miro.medium.com/v2/resize:fit:702/1*Ra02AqsQlC0KV229EvM98g.png", use_container_width=True)
st.sidebar.info("Explore insights from the uploaded data.")
uploaded_file = st.file_uploader("Upload Excel or CSV file:", type=["xlsx", "csv"])
if uploaded_file is not None:
with tempfile.NamedTemporaryFile(delete=False) as tmp_file:
tmp_file.write(uploaded_file.read())
tmp_file_path = tmp_file.name
# Load Excel or CSV file
if uploaded_file.name.endswith(".xlsx"):
df = pd.read_excel(tmp_file_path)
elif uploaded_file.name.endswith(".csv"):
df = pd.read_csv(tmp_file_path)
st.write("### Uploaded Data:")
st.dataframe(df.head(len(df)))
st.subheader("Exploratory Data Analysis (EDA):")
st.write("Please wait, reports are generating...")
response = ProfileReport(df)
response.to_file("data_profile_report.html")
with open("data_profile_report.html", "r", encoding="utf-8") as f:
data = f.read()
components.html(data, width=800, height=600, scrolling=True)
# Main UI layout
def main():
st.sidebar.image("https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcSZYA5fOIfm6K6v3Lrro3MXksMfO3SdglfSyg&s", use_container_width=True)
st.title("DocTalk : Chat with Excel/CSV")
st.sidebar.title("Options")
selected_option = st.sidebar.radio("Select an option:", ("Chat with Excel/CSV", "EDA"))
# Take user API key input
api_key = st.sidebar.text_input("Enter OpenAI API Key:", type="password")
if api_key:
if selected_option == "Chat with Excel/CSV":
code_for_option_1(api_key)
elif selected_option == "EDA":
code_for_option_2()
else:
st.write("Please select an option.")
else:
st.sidebar.warning("Please enter your OpenAI API key to proceed.")
if __name__ == "__main__":
main()
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