Update app.py

app.py

@@ -115,7 +115,7 @@ with st.sidebar:
 uploaded_file = "bon_marche.csv"
 #uploaded_file = "healthcare_dataset.csv"
 if tabs =='Chat':
-    st.header("Brave Retail Chat")
+    st.subheader("Brave Retail Chat")
     st.write("Get visualizations and analysis from our Gemini powered agent")
         
         # Read the CSV file
@@ -133,13 +133,48 @@ if tabs =='Chat':
 
 elif tabs == 'Reports':
     df = pd.read_csv(uploaded_file)
-    st.header("Reports")
-    prompt = """
+    
+
+# Streamlit App
+    st.subheader("Reports")
+    st.write("Filter by Branch Name or Product to generate report")
+
+# Display original 
+
+# Filtering Interface
+    st.write("Filtering Options")
+    branch_names = df['Branch_Name'].unique().tolist()
+    product_names = df['Description'].unique().tolist()
+    selected_branch = st.selectbox('Select Branch Name', ['All'] + branch_names)
+    selected_product = st.selectbox('Select Product', ['All'] + product_names)
+
+# Button to apply filters
+    if st.button('Apply Filters'):
+        filtered_df = df.copy()
+    
+    # Apply Branch Name Filter
+    if selected_branch!= 'All':
+        filtered_df = filtered_df[filtered_df['Branch_Name'] == selected_branch]
+    
+    # Apply Description Filter
+    if description_filter:
+        filtered_df = filtered_df[filtered_df['Description'] == selected_product]
+    
+    # Display filtered DataFrame
+        st.write("Filtered DataFrame")
+        with st.expander("Preview"):
+            st.write(filtered_df)
+        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.
 
 data:
-""" + str(calculate_kpis(df)) + str(get_pandas_profile(df))
+""" + str(calculate_kpis(filtered_df)) + str(get_pandas_profile(filtered_df))
 
-    response = model.generate_content(prompt)
-    report = response.text
-    st.markdown(report)
+        response = model.generate_content(prompt)
+        report = response.text
+        st.markdown(report)
+    else:
+        st.write("Filtered DataFrame")
+        st.write("Click 'Apply Filters' to see the filtered data.")
+        
+