Update app.py

app.py

@@ -18,14 +18,12 @@ class DataLoader:
         self.sample_file_url = sample_file_url
 
     @st.cache_data
-    # _self is correct for the instance itself
-    def load_csv(_self, uploaded_file_obj: st.runtime.uploaded_file_manager.UploadedFile | None) -> pd.DataFrame | None:
+    def load_csv(self, uploaded_file_obj: st.runtime.uploaded_file_manager.UploadedFile | None) -> pd.DataFrame | None:
         """
         Loads the GSC data from an uploaded CSV or a sample URL,
         normalizes column names, and ensures a 'cpc' column exists.
-
         Args:
-            _self: The instance of the DataLoader class (ignored by Streamlit caching).
+            self: The instance of the DataLoader class.
             uploaded_file_obj (streamlit.runtime.uploaded_file_manager.UploadedFile): The file object
                                                                              uploaded by the user, or None.
         Returns:
@@ -35,8 +33,8 @@ class DataLoader:
             if uploaded_file_obj:
                 df = pd.read_csv(uploaded_file_obj)
             else:
-                # Use _self.sample_file_url since _self is the instance
-                df = pd.read_csv(_self.sample_file_url)
+                # Use self.sample_file_url since self is the instance
+                df = pd.read_csv(self.sample_file_url)
         except Exception as e:
             st.error(f"Error loading file: {e}")
             return None
@@ -83,9 +81,8 @@ class SeoCalculator:
         return df.rename(columns={found_columns[k]: k for k in found_columns})
 
     @st.cache_data
-    # _self is correct for the instance itself
     def calculate_metrics(
-        _self, # Changed to _self
+        self,
         df: pd.DataFrame,
         target_position: float,
         conversion_rate: float,
@@ -96,18 +93,16 @@ class SeoCalculator:
     ) -> tuple[dict, pd.DataFrame] | tuple[None, pd.DataFrame]:
         """
         Performs core calculations for SEO forecasting based on GSC data and user inputs.
-
         Returns:
             tuple: A dictionary of calculated metrics and a DataFrame with detailed results.
                    Returns (None, pd.DataFrame()) if required columns are missing.
         """
-        # Use _self.ctr_benchmarks and _self.required_columns_map, etc.
-        df_processed = _self._validate_and_rename_columns(df.copy())
+        df_processed = self._validate_and_rename_columns(df.copy())
         if df_processed is None:
             return None, pd.DataFrame()
 
-        df_processed["current_ctr"] = df_processed["position"].apply(_self._get_ctr)
-        target_ctr_value = _self._get_ctr(target_position)
+        df_processed["current_ctr"] = df_processed["position"].apply(self._get_ctr)
+        target_ctr_value = self._get_ctr(target_position)
         df_processed["target_ctr"] = target_ctr_value
 
         df_processed["current_clicks"] = df_processed["impressions"] * df_processed["current_ctr"]
@@ -199,6 +194,7 @@ class SeoAppUI:
     def _get_sidebar_inputs(self) -> tuple:
         with st.sidebar:
             st.header("🔧 Assumptions & Inputs")
+            # The upload file will remain in the sidebar as it's an input
             uploaded_file = st.file_uploader("Upload queries CSV data", type="csv")
             target_position = st.slider(
                 "Target SERP Position",
@@ -214,13 +210,6 @@ class SeoAppUI:
             seo_cost = st.slider("Total SEO Investment ($)", 1_000, 100_000, 10_000, 1_000)
             add_spend = st.slider("Additional Ad Spend ($)", 0, 50_000, 0, 1_000, help="A **hypothetical budget** for extra paid ad spend, not from your GSC data. Use it to directly compare SEO's projected incremental MRR with a potential ad investment.")
 
-            sample_bytes = requests.get(SAMPLE_FILE_URL).content
-            st.download_button(
-                label="📥 Download sample CSV",
-                data=sample_bytes,
-                file_name="sample_gsc_data.csv",
-                mime="text/csv",
-            )
         return uploaded_file, target_position, conversion_rate, close_rate, mrr_per_customer, seo_cost, add_spend
 
     def _display_summary_metrics(self, metrics: dict):
@@ -293,13 +282,23 @@ class SeoAppUI:
 
     def run(self):
         self._display_info_expander()
+
+        # Moved the download button out of the sidebar to the main area
+        sample_bytes = requests.get(SAMPLE_FILE_URL).content
+        st.download_button(
+            label="📥 Download sample CSV",
+            data=sample_bytes,
+            file_name="sample_gsc_data.csv",
+            mime="text/csv",
+            key="download_sample_main" # Added a key to avoid potential duplicate widget issues
+        )
+        st.markdown("---") # Add a separator for better visual organization
+
         uploaded_file, target_position, conversion_rate, close_rate, mrr_per_customer, seo_cost, add_spend = self._get_sidebar_inputs()
 
-        # FIX: Call load_csv normally, Python handles the _self
         df = self.data_loader.load_csv(uploaded_file)
 
         if df is not None:
-            # FIX: Call calculate_metrics normally, Python handles the _self
             metrics, df_results = self.seo_calculator.calculate_metrics(
                 df,
                 target_position,