Update app.py

app.py

@@ -7,6 +7,7 @@ import seaborn as sns
 from sklearn.cluster import KMeans
 from statsmodels.tsa.holtwinters import ExponentialSmoothing
 from langchain_openai import ChatOpenAI
+from pandasai import SmartDatalake
 from dotenv import load_dotenv
 
 # ✅ Load Environment Variables
@@ -43,6 +44,10 @@ st.set_page_config(page_title="AI Store Replacement, Demand Prediction & Chatbot
 # ✅ Title
 st.title("🔄 AI-Powered Store Replacement, Demand Prediction & Chatbot")
 
+# ✅ Data Preview Section
+st.subheader("📊 Full Data Preview - KPI Sheet")
+st.dataframe(df)
+
 # ✅ Store Replacement System
 st.subheader("🏬 AI-Based Store Replacement")
 
@@ -86,17 +91,11 @@ if selected_mall:
         existing_stores = set(mall_data["Store_Brand"].unique())
         potential_replacements = list(all_stores - existing_stores)
 
-        # ✅ Debugging: Check available replacements
-        st.write(f"🔍 **Available Replacement Stores:** {potential_replacements}")
-
         if potential_replacements:
             replacement_prompt = f"Based on the category {store_category}, suggest 3 stores from the list below that are not already in {selected_mall}. The stores should match the mall’s customer base. Store List: {potential_replacements}"
             replacement_response = llm.invoke(replacement_prompt).content
             replacement_stores = replacement_response.split(", ")
 
-            # ✅ Debugging: Print AI Response
-            st.write(f"🧠 **AI Raw Response:** {replacement_response}")
-
             st.success(f"🔄 **Recommended Replacement Stores:**")
             for replacement_store in replacement_stores:
                 store_data = df[df["Store_Brand"] == replacement_store]
@@ -114,31 +113,33 @@ if selected_mall:
                         expected_margin = store_data["Gross_Margin"].mean()
 
                         st.write(f"**{replacement_store}**")
-                        st.write(f"📊 **Predicted Sales:** AED {forecast_sales:,.2f}")
-                        st.write(f"👣 **Expected Footfall:** {expected_footfall:,.0f} visitors")
-                        st.write(f"💰 **Expected Margin:** AED {expected_margin:,.2f}")
-                        st.write(f"🔄 **Expected Conversion Rate:** {expected_conversion:.2f}%")
+                        st.write(f"📊 **Projected Sales:** AED {forecast_sales:,.2f}")
+                        st.write(f"👣 **Projected Footfall:** {expected_footfall:,.0f} visitors")
+                        st.write(f"💰 **Projected Margin:** AED {expected_margin:,.2f}")
+                        st.write(f"🔄 **Projected Conversion Rate:** {expected_conversion:.2f}%")
 
                         # ✅ Justification
-                        justification_prompt = f"Explain why {replacement_store} is a good replacement for {store_to_replace} in {selected_mall}. Consider factors like category relevance, expected sales, and competitor presence."
+                        justification_prompt = f"Explain why {replacement_store} is a good replacement for {store_to_replace} in {selected_mall}. Include numeric KPIs: expected sales, footfall, conversion rate, and profit margins."
                         justification = llm.invoke(justification_prompt).content
                         st.write(f"🤖 **AI Justification:** {justification}")
 
 # ✅ AI Chatbot for Dynamic Insights
 st.subheader("💬 AI-Powered Chatbot")
 
-# AI Chatbot using LangChain
-user_query = st.text_input("🔍 Ask anything about store performance, competitors, or pricing:")
+# AI Chatbot using PandasAI
+llm_pandas = ChatOpenAI(model="gpt-3.5-turbo", api_key=openai_api_key)
+sdl = SmartDatalake([df], config={"llm": llm_pandas, "enable_cache": False})
+
+user_query = st.text_input("🔍 Ask AI About Your Data:")
 if user_query:
     with st.spinner("🤖 AI is thinking..."):
         try:
-            chat_prompt = f"Answer the following retail business question based on your market knowledge: {user_query}"
-            chat_response = llm.invoke(chat_prompt).content
-            st.markdown(f"🤖 **AI Response:** {chat_response}")
+            response = sdl.chat(user_query)
+            st.markdown(f"🤖 **AI:** {response}")
 
         except Exception as e:
             st.error(f"❌ AI Error: {str(e)}")
 
 # ✅ Footer
 st.markdown("---")
-st.markdown("💡 Built with ❤️ using **LangChain AI for Store Replacement & Demand Prediction**")
+st.markdown("💡 Built with ❤️ using **AI-Powered Store Replacement & Demand Prediction**")