CSV DATA Added

app_csv.py

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+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")