AutomatiX-Deport added

app.py

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+import streamlit as st
+import os
+import psycopg2 as pgsql
+import pandas as pd
+import plotly.express as px
+from dotenv import load_dotenv
+import google.generativeai as genai
+
+# Load environment variables
+load_dotenv()
+
+# Configure Genai Key
+genai.configure(api_key=os.getenv("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 retrieve query from the database
+def read_sql_query(sql, db_params):
+    try:
+        conn = pgsql.connect(**db_params)
+        cur = conn.cursor()
+        cur.execute(sql)
+        rows = cur.fetchall()
+        colnames = [desc[0] for desc in cur.description] if cur.description else []
+        conn.commit()
+        cur.close()
+        conn.close()
+        df = pd.DataFrame(rows, columns=colnames)
+        
+        # Convert 'price' column to numeric if it exists
+        if 'price' in df.columns:
+            df['price'] = pd.to_numeric(df['price'], errors='coerce')
+        
+        return df
+    except Exception as e:
+        st.error(f"An error occurred: {e}")
+        return pd.DataFrame()
+
+# Define your PostgreSQL connection parameters
+db_params = {
+    'dbname': 'GeminiPro',
+    'user': 'postgres',
+    'password': 'root',
+    'host': 'localhost',
+    'port': 5432
+}
+
+# Define Your Prompt
+prompt = [
+    """
+    You are an expert in converting English questions to SQL queries!
+    The SQL database has a table named 'department_store' with the following columns: 
+    id, product_name, category, price, stock_quantity, supplier, last_restock_date.
+    
+    Examples:
+    - How many products do we have in total? 
+      The SQL command will be: SELECT COUNT(*) FROM department_store;
+    - What are all the products in the Electronics category? 
+      The SQL command will be: SELECT * FROM department_store WHERE category = 'Electronics';
+    
+    The SQL code should not include backticks and should not start with the word 'SQL'.
+    """
+]
+
+# Streamlit App
+st.set_page_config(page_title="AutomatiX - Department Store Analytics", layout="wide")
+
+# Sidebar for user input
+st.sidebar.title("AutomatiX - Department Store Chat Interface")
+question = st.sidebar.text_area("Enter your question:", key="input")
+submit = st.sidebar.button("Ask Me")
+
+# Main content area
+st.title("AutomatiX - Department Store Dashboard")
+
+if submit:
+    with st.spinner("Generating and fetching data..."):
+        sql_query = get_gemini_response(question, prompt)
+        # st.code(sql_query, language="sql")
+        
+        df = read_sql_query(sql_query, db_params)
+        
+        if not df.empty:
+            st.success("Query executed successfully!")
+            
+            # Display data in a table
+            st.subheader("Data Table")
+            st.dataframe(df)
+            
+            # Create visualizations based on the data
+            st.subheader("Data Visualizations")
+            
+            col1, col2 = st.columns(2)
+            
+            with col1:
+                if 'price' in df.columns and df['price'].notna().any():
+                    fig = px.histogram(df, x='price', title='Price Distribution')
+                    st.plotly_chart(fig, use_container_width=True)
+                
+                if 'category' in df.columns:
+                    category_counts = 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 df.columns:
+                    df['last_restock_date'] = pd.to_datetime(df['last_restock_date'], errors='coerce')
+                    df['restock_month'] = df['last_restock_date'].dt.to_period('M')
+                    restock_counts = 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 df.columns and 'price' in df.columns and df['price'].notna().any():
+                    top_prices = 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.info("You can ask questions like:\n"
+"1.What are all the products in the Electronics category?\n"
+"2.What is the average price of products in each category?\n"
+"3.Which products have a stock quantity less than 30?\n"
+"4.What are the top 5 most expensive products?")
+st.sidebar.warning("CopyRights@AutomatiX - Powered by Streamlit and Google Gemini")

requirements.txt

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+streamlit
+google-generativeai
+python-dotenv
+psycopg2-binary
+mysql-connector-python
+pandas