app.py

import streamlit as st
import os
import pandas as pd
import plotly.express as px
import google.generativeai as genai
from io import StringIO

# Configure Genai Key
genai.configure(api_key=os.environ.get("GOOGLE_API_KEY"))

# Function to load Google Gemini Model and provide queries as response
def get_gemini_response(question, prompt):
    model = genai.GenerativeModel('gemini-pro')
    response = model.generate_content([prompt[0], question])
    return response.text.strip()

# Function to load data from CSV
@st.cache_data
def load_data():
    # This is a sample CSV content. In practice, you'd read this from a file.
    csv_content = """

id,product_name,category,price,stock_quantity,supplier,last_restock_date

1,Cotton T-Shirt,Clothing,19.99,100,FashionCo,2024-03-01

2,Denim Jeans,Clothing,49.99,75,DenimWorld,2024-02-15

3,Running Shoes,Footwear,79.99,50,SportyFeet,2024-03-10

4,Leather Wallet,Accessories,29.99,30,LeatherCrafts,2024-01-20

5,Smartphone Case,Electronics,14.99,200,TechProtect,2024-03-05

6,Coffee Maker,Appliances,89.99,25,KitchenTech,2024-02-28

7,Yoga Mat,Sports,24.99,40,YogaEssentials,2024-03-15

8,Backpack,Bags,39.99,60,TravelGear,2024-02-10

9,Sunglasses,Accessories,59.99,35,ShadesMaster,2024-03-20

10,Bluetooth Speaker,Electronics,69.99,45,SoundWave,2024-01-30

"""
    df = pd.read_csv(StringIO(csv_content))
    df['price'] = pd.to_numeric(df['price'], errors='coerce')
    df['last_restock_date'] = pd.to_datetime(df['last_restock_date'], errors='coerce')
    return df

# Function to execute pandas query
def execute_pandas_query(df, query):
    try:
        # This is a very simple and unsafe way to execute queries.
        # In a real application, you'd need to parse the SQL and translate it to pandas operations.
        result = eval(f"df.{query}")
        return result
    except Exception as e:
        st.error(f"An error occurred: {e}")
        return pd.DataFrame()

# Define Your Prompt
prompt = [
    """

    You are an expert in converting English questions to pandas DataFrame operations!

    The DataFrame 'df' has the following columns: 

    id, product_name, category, price, stock_quantity, supplier, last_restock_date.

    

    Examples:

    - How many products do we have in total? 

      The pandas operation will be: len()

    - What are all the products in the Electronics category? 

      The pandas operation will be: query("category == 'Electronics'")

    

    The pandas operation should be a valid Python expression that can be applied to a DataFrame 'df'.

    """
]

# Streamlit App
st.set_page_config(page_title="Department Store Analytics", layout="wide")

# Load data
df = load_data()

# Sidebar for user input
st.sidebar.title("Department Store Query Interface")
question = st.sidebar.text_area("Enter your question:", key="input")
submit = st.sidebar.button("Ask Me")

# Main content area
st.title("Department Store Dashboard")

if submit:
    with st.spinner("Generating query and fetching data..."):
        pandas_query = get_gemini_response(question, prompt)
        st.code(pandas_query, language="python")
        
        result_df = execute_pandas_query(df, pandas_query)
        
        if not result_df.empty:
            st.success("Query executed successfully!")
            
            # Display data in a table
            st.subheader("Data Table")
            st.dataframe(result_df)
            
            # Create visualizations based on the data
            st.subheader("Data Visualizations")
            
            col1, col2 = st.columns(2)
            
            with col1:
                if 'price' in result_df.columns and result_df['price'].notna().any():
                    fig = px.histogram(result_df, x='price', title='Price Distribution')
                    st.plotly_chart(fig, use_container_width=True)
                
                if 'category' in result_df.columns:
                    category_counts = result_df['category'].value_counts()
                    fig = px.pie(values=category_counts.values, names=category_counts.index, title='Products by Category')
                    st.plotly_chart(fig, use_container_width=True)
            
            with col2:
                if 'last_restock_date' in result_df.columns:
                    result_df['restock_month'] = result_df['last_restock_date'].dt.to_period('M')
                    restock_counts = result_df['restock_month'].value_counts().sort_index()
                    fig = px.line(x=restock_counts.index.astype(str), y=restock_counts.values, title='Restocking Trend')
                    st.plotly_chart(fig, use_container_width=True)
                
                if 'product_name' in result_df.columns and 'price' in result_df.columns and result_df['price'].notna().any():
                    top_prices = result_df.sort_values('price', ascending=False).head(10)
                    fig = px.bar(top_prices, x='product_name', y='price', title='Top 10 Most Expensive Products')
                    st.plotly_chart(fig, use_container_width=True)
        else:
            st.warning("No data returned from the query.")

else:
    st.info("Enter a question and click 'Ask Me' to get started!")

# Footer
st.sidebar.markdown("---")
st.sidebar.warning("AutomatiX - Department Store Analytics - Powered by Streamlit and Google Gemini")