Update app.py

app.py

@@ -4,14 +4,14 @@ import numpy as np
 import os
 import matplotlib.pyplot as plt
 import seaborn as sns
-from sklearn.cluster import KMeans
+from dotenv import load_dotenv
 from langchain_openai import ChatOpenAI
 from langchain.chains import LLMChain
 from langchain.prompts import PromptTemplate
-from dotenv import load_dotenv
 from pycaret.regression import setup as setup_reg, compare_models, predict_model
 from pandasai import SmartDatalake
 from pandasai.llm import OpenAI
+from crewai import Crew, Agent, Task
 
 # ✅ Load Environment Variables
 load_dotenv()
@@ -49,78 +49,54 @@ store_categories = {
                "REN by Sushi Library", "Sushi Library", "Tim Hortons", "LM Exchange"]
 }
 
+# ✅ CrewAI Agents & Tasks
+cleaning_agent = Agent(
+    role="Data Cleaning Agent",
+    goal="Ensure data quality by handling missing values and duplicates.",
+    backstory="An expert in data preprocessing, ensuring high-quality input for analysis.",
+    llm=ChatOpenAI(model="gpt-4"),
+    allow_delegation=True
+)
+
+competitor_agent = Agent(
+    role="Competitor Analysis Agent",
+    goal="Fetch competitors for the selected mall or generate AI-based fallback.",
+    llm=ChatOpenAI(model="gpt-4"),
+    allow_delegation=True
+)
+
+replacement_agent = Agent(
+    role="Store Replacement Agent",
+    goal="Find the best replacement stores matching the selected store’s category.",
+    llm=ChatOpenAI(model="gpt-4"),
+    allow_delegation=True
+)
+
+prediction_agent = Agent(
+    role="KPI Prediction Agent",
+    goal="Use AutoML to predict sales, footfall, conversion rate, and margins.",
+    llm=ChatOpenAI(model="gpt-4"),
+    allow_delegation=True
+)
+
+chatbot_agent = Agent(
+    role="Conversational AI Agent",
+    goal="Answer business-related queries about the dataset.",
+    llm=ChatOpenAI(model="gpt-4"),
+    allow_delegation=True
+)
+
+crew = Crew(agents=[cleaning_agent, competitor_agent, replacement_agent, prediction_agent, chatbot_agent])
+
 # ✅ Streamlit Page Configuration
 st.set_page_config(page_title="AI Store Replacement & Chatbot", layout="wide")
 
 # ✅ Title
-st.title("🔄 AI-Powered Store Replacement, AutoML Demand Prediction & Smart Data Lake AI")
-
-# ✅ Data Preview Section
-st.subheader("📊 Full Data Preview - KPI Sheet")
-st.dataframe(df)
-
-# ✅ Store Replacement System
-st.subheader("🏬 AI-Based Store Replacement")
-
-# Select Mall
-selected_mall = st.selectbox("🏬 Select a Mall:", df["Mall"].unique())
-
-if selected_mall:
-    # Get all stores in the selected mall
-    mall_data = df[df["Mall"] == selected_mall]
-    
-    # Select the store that will be replaced
-    store_to_replace = st.selectbox("🚪 Select the store that is closing:", mall_data["Store_Brand"].unique())
-
-    if store_to_replace:
-        llm = ChatOpenAI(model="gpt-4", api_key=openai_api_key)
-
-        # ✅ Identify Store Category
-        store_category = None
-        for category, stores in store_categories.items():
-            if store_to_replace in stores:
-                store_category = category
-                break
-
-        if not store_category:
-            store_category = "Others"
-
-        st.write(f"🔍 **Identified Category:** {store_category}")
-
-        # ✅ Find Replacement Stores
-        existing_stores = set(mall_data["Store_Brand"].unique())
-        potential_replacements = [store for store in store_categories.get(store_category, []) if store not in existing_stores]
-
-        if potential_replacements:
-            st.success(f"🔄 **Recommended Replacement Stores:**")
-            for replacement_store in potential_replacements[:3]:
-                store_data = df[df["Store_Brand"] == replacement_store]
-
-                if not store_data.empty:
-                    # ✅ AutoML Model Selection
-                    setup_reg(df, target="Annual_Sales", silent=True, verbose=False)
-                    best_model = compare_models()
-                    projected_sales = predict_model(best_model, data=store_data)["Label"].mean()
-
-                    setup_reg(df, target="Footfall", silent=True, verbose=False)
-                    best_model = compare_models()
-                    projected_footfall = predict_model(best_model, data=store_data)["Label"].mean()
-
-                    setup_reg(df, target="Gross_Margin", silent=True, verbose=False)
-                    best_model = compare_models()
-                    projected_margin = predict_model(best_model, data=store_data)["Label"].mean()
-
-                    setup_reg(df, target="Conversion_Rate", silent=True, verbose=False)
-                    best_model = compare_models()
-                    projected_conversion = predict_model(best_model, data=store_data)["Label"].mean()
-
-                    # ✅ Display KPIs
-                    st.subheader(f"📊 Projected KPIs for {replacement_store}")
-                    st.write(f"🏆 **Best Model Used:** {best_model}")
-                    st.write(f"📊 **Projected Sales:** AED {projected_sales:,.2f}")
-                    st.write(f"👣 **Projected Footfall:** {projected_footfall:,.0f} visitors")
-                    st.write(f"💰 **Projected Margin:** AED {projected_margin:,.2f}")
-                    st.write(f"🔄 **Projected Conversion Rate:** {projected_conversion:.2f}%")
+st.title("🔄 AI-Powered Store Replacement, AutoML Demand Prediction & CrewAI")
+
+# ✅ Data Preview (First 10 Rows)
+st.subheader("📊 Sample Data Preview - KPI Sheet")
+st.dataframe(df.head(10))
 
 # ✅ AI-Powered Conversational Smart Data Lake
 st.subheader("💬 AI-Powered Data Chatbot")
@@ -136,3 +112,9 @@ if user_query:
             st.markdown(f"🤖 **AI:** {response}")
         except Exception as e:
             st.error(f"❌ AI Error: {str(e)}")
+
+# ✅ CrewAI Execution
+st.subheader("🚀 AI Task Execution with CrewAI")
+st.write("Executing all AI agents...")
+crew.kickoff()
+st.success("✅ CrewAI execution completed!")