Initial commit

app.py

@@ -192,7 +192,10 @@ def ev_insights():
 
 
 def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=2009, end_year=2025):
+<<<<<<< HEAD
     
+=======
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b
     # Folder paths
     folders = {
         "statewise": os.path.join(root_dir, "Fuel_vs_state"),
@@ -229,6 +232,10 @@ def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=20
             combined_df = pd.concat([combined_df, df], ignore_index=True)
         return combined_df
 
+<<<<<<< HEAD
+=======
+    # Load all data
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b
     statewise_df = load_data_for_year_range(folders["statewise"], start_year, end_year)
     norms_df = load_data_for_year_range(folders["norms"], start_year, end_year)
     fuelwise_df = load_data_for_year_range(folders["fuelwise"], start_year, end_year)
@@ -241,6 +248,10 @@ def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=20
         "🔥 Emission by Fuel Type"
     ])
 
+<<<<<<< HEAD
+=======
+    # Tab 1
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b
     with tab1:
         st.subheader("Fuel Distribution Across States")
         if not statewise_df.empty:
@@ -260,6 +271,10 @@ def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=20
                           labels={"Fuel_Amount": "Amount (in units)"}, barmode="group")
             st.plotly_chart(fig1, use_container_width=True)
 
+<<<<<<< HEAD
+=======
+    # Tab 2
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b
     with tab2:
         st.subheader("Emission Norm Distribution by State")
         if not norms_df.empty:
@@ -279,6 +294,7 @@ def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=20
                           labels={"Count": "Count of Norms"}, barmode="group")
             st.plotly_chart(fig2, use_container_width=True)
 
+<<<<<<< HEAD
     with tab3:
         st.subheader("Norm Emissions by Fuel Type and Year")
 
@@ -316,6 +332,37 @@ def fuel_norm_distribution_dashboard(root_dir, state_mapping_file, start_year=20
                 norm_vs_category_combined = pd.concat([norm_vs_category_combined, norm_df], ignore_index=True)
             except Exception as e:
                 st.warning(f"Error loading data for {year}: {e}")
+=======
+    # Tab 3
+    with tab3:
+        st.subheader("Norm Emissions by Fuel Type")
+        if not fuelwise_df.empty:
+            norm_columns = [col for col in fuelwise_df.columns if col not in ["Fuel", "Year"]]
+            if "Fuel" in fuelwise_df.columns and norm_columns:
+                melted_fuelwise = pd.melt(fuelwise_df, id_vars=["Fuel", "Year"],
+                                          var_name="Norm_Type", value_name="Emission")
+                melted_fuelwise = melted_fuelwise.dropna()
+                melted_fuelwise["Emission"] = pd.to_numeric(melted_fuelwise["Emission"], errors="coerce")
+                melted_fuelwise = melted_fuelwise.dropna(subset=["Emission"])
+
+                selected_fuels = st.multiselect("Select Fuel Types", ["Select All"] + sorted(melted_fuelwise["Fuel"].unique()), default=[], key="fuel_selection")
+                if "Select All" in selected_fuels:
+                    selected_fuels = list(melted_fuelwise["Fuel"].unique())
+
+                year_range = st.slider("Select Year Range", min_value=start_year, max_value=end_year, value=(start_year, end_year), key="fuel_year")
+                filtered_fuelwise = melted_fuelwise[(melted_fuelwise["Fuel"].isin(selected_fuels)) &
+                                                    (melted_fuelwise["Year"].between(year_range[0], year_range[1]))]
+
+                fig3 = px.bar(filtered_fuelwise, x="Fuel", y="Emission", color="Norm_Type",
+                              title=f"Emission per Fuel Type from {year_range[0]} to {year_range[1]}",
+                              labels={"Emission": "Emission Amount"}, barmode="stack")
+                st.plotly_chart(fig3, use_container_width=True)
+
+                st.subheader("Top Fuels Emitting Most Pollution")
+                top_fuels = filtered_fuelwise.groupby("Fuel")["Emission"].sum().reset_index()
+                top_fuels_sorted = top_fuels.sort_values(by="Emission", ascending=False).head(10)
+                st.write(top_fuels_sorted)
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b
 
         fuel_options = sorted(fuel_vs_norm_combined['fuel'].dropna().unique())
         category_options = sorted(norm_vs_category_combined['vehicle_category'].dropna().unique())
@@ -671,9 +718,16 @@ def ask_with_llm():
 
     # Download option
     csv = result.to_csv(index=False).encode('utf-8')
+<<<<<<< HEAD
     st.download_button("⬇️ Download Result as CSV", data=csv, file_name="query_result.csv", mime='text/csv')
 
     st.plotly_chart(fig)
+=======
+    st.download_button("⬇ Download Result as CSV", data=csv, file_name="query_result.csv", mime='text/csv')
+
+    st.plotly_chart(fig)
+    
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b
     
 
 # Run the entire app
@@ -708,4 +762,8 @@ elif section == "Ask with Text (LLM)":
     st.markdown("- Norm-wise registrations in Maharashtra in 2024 ")
     st.markdown("- Electric (EV) norm-wise registrations in Karnataka after 2021 ")
     st.markdown("- Fuel-wise registrations in Delhi in 2020")
+<<<<<<< HEAD
     st.markdown("- Vehicle classes by registrations in Tamil Nadu in 2022 ")
+=======
+    st.markdown("- Vehicle classes by registrations in Tamil Nadu in 2022 ")   
+>>>>>>> b8597d17346ea9ab17065cb28941f11c8f958d1b