Update app.py

app.py

@@ -37,54 +37,6 @@ model = genai.GenerativeModel(
   generation_config=generation_config,
 )
 
-def calculate_kpis(df):
-    """
-    Calculates key performance indicators from a given transaction dataset.
-
-    Args:
-        df: Pandas DataFrame containing transaction data.
-
-    Returns:
-        A JSON object containing the calculated KPIs.
-    """
-
-    # Calculate Total Revenue
-    total_revenue = df['Price'] * df['Quantity'].sum()
-
-    # Calculate Top Five Products by Revenue
-    if df['Description'].nunique() > 5:
-        top_five_products = df.groupby('Description')['Price'].sum().nlargest(5).index.tolist()
-    else:
-        top_five_product = "there are less than 5 products in this dataset"
-
-    if df['Branch_Name'].nunique() > 1:
-        best_branch = df.groupby('Branch_Name')['Price'].sum().nlargest(1).index.tolist()
-    else:
-        best_branch = "there is only one branch in this dataset"
-        
-    # Calculate Average Order Value (AOV)
-    aov = df.groupby('Receipt No_')['Price'].sum().mean()
-
-    # Calculate Customer Purchase Frequency (Requires more data for accurate calculation)
-    # Assuming 'Member Card No_' is a unique identifier for customers
-    customer_purchase_frequency = df.groupby('Customer_Name')['Receipt No_'].nunique().mean()
-
-    # Calculate Estimated Customer Lifetime Value (CLTV) (Requires more data for accurate calculation)
-    # Assuming a simple CLTV model based on AOV and purchase frequency
-    estimated_cltv = aov * customer_purchase_frequency * 12  # Assuming annual value
-
-    # Create JSON output
-    kpis = {
-        "total_revenue": total_revenue,
-        "top_five_products": top_five_products,
-        "average_order_value": aov,
-        "customer_purchase_frequency": customer_purchase_frequency,
-        "estimated_cltv": estimated_cltv,
-        "best_performing_branch": best_branch
-    }
-
-    return kpis
-
 
 def get_pandas_profile(df):
     profile = ProfileReport(df, title="Profiling Report")
@@ -102,9 +54,9 @@ def generateResponse(dataFrame,prompt):
         answer = pandas_agent.chat(prompt)
         return answer
 
-st.write("# Brave Retail Insights")
+st.write("# QuantBeta Insights")
 st.markdown('<style>' + open('./style.css').read() + '</style>', unsafe_allow_html=True)
-st.write("##### Engage in insightful conversations with your data through powerful visualizations")
+st.write("##### Engage in insightful conversations with your data")
 with st.sidebar:
     st.title("Brave Retail Insights")
     st.sidebar.image("IMG_1181.jpeg", use_column_width=True)
@@ -113,11 +65,11 @@ with st.sidebar:
         
 
 
-uploaded_file = "bon_marche.csv"
+uploaded_file = "64QBeta.csv"
 #uploaded_file = "healthcare_dataset.csv"
 if tabs =='Chat':
     df = pd.read_csv(uploaded_file)
-    st.subheader("Brave Retail Chat")
+    st.subheader("QuantBeta AI assistant")
     st.write("Get visualizations and analysis from our Gemini powered agent")
         
         # Read the CSV file
@@ -139,15 +91,15 @@ elif tabs == 'Reports':
 
 # Streamlit App
     st.subheader("Reports")
-    st.write("Filter by Branch Name or Product to generate report")
+    st.write("Filter by Incubator to generate report")
 
 # Display original 
 
 # Filtering Interface
     st.write("Filtering Options")
-    branch_names = df['Branch_Name'].unique().tolist()
+    branch_names = df['Incubator Name'].unique().tolist()
     #product_names = df['Description'].unique().tolist()
-    selected_branches = st.multiselect('Select Branch(es) Name', branch_names, default=branch_names)
+    selected_branches = st.multiselect('Select incubator(s) Name(s)', branch_names, default=branch_names)
     #selected_products = st.multiselect('Select product(s) Name', product_names, default=product_names)
 
 # Button to apply filters
@@ -157,7 +109,7 @@ elif tabs == 'Reports':
     
     # Apply Branch Name Filter
         if selected_branches:
-            filtered_df = filtered_df[filtered_df['Branch_Name'].isin(selected_branches)]
+            filtered_df = filtered_df[filtered_df['Incubator Name'].isin(selected_branches)]
     
     # Apply Description Filter
     #if selected_products:
@@ -169,19 +121,17 @@ elif tabs == 'Reports':
                 st.write(filtered_df.head())
             with st.spinner("Generating Report, Please Wait...."):
                 prompt = """
-You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, including appropriate key perfomance indicators and reccomendations.
+You are an expert business analyst. Analyze the following data and generate a comprehensive and insightful business report, including appropriate key perfomance indicators and recommendations.
 
 data:
-"""  +  str(calculate_kpis(filtered_df)) + str(get_pandas_profile(filtered_df))
+"""  +  str(filtered_df.to_json(orient='records')) + str(get_pandas_profile(filtered_df))
 
                 response = model.generate_content(prompt)
-                response2 = generateResponse(filtered_df, "pie chart of revenue by branch")
+                response2 = generateResponse(filtered_df, "pie chart of number of hours  by branch")
                 response3 = generateResponse(filtered_df, "bar chart of of most popular products")
                 report = response.text
                 st.markdown(report)
                 # Display the generated images
-                st.markdown(response2)
-                st.markdown(response3)
                 st.success("Report Generated!")
         else:
             st.write("Filtered DataFrame")