Use proper conditioning

app.py

@@ -46,23 +46,44 @@ def last_thursday(dt):
 
 
 def check_condition_passed(df, column_name, max_index, output_value, close_column, value1, value2, is_high = True):
-    if is_high:
-        filtered_df = df[(df.index < max_index) & (df[column_name] > output_value)]
-    else:
-        filtered_df = df[(df.index < max_index) & (df[column_name] < output_value)]
-    
-    if not filtered_df.empty:
-        first_valid_row = filtered_df.iloc[0]
-        close_value = first_valid_row[close_column]
-
+    # print(f"Max index: {max_index}")
+    for index_maybe in range(max_index-1, -1, -1):
+        # print(f"At index : {index_maybe}")
         if is_high:
-            if close_value > value1 and close_value > value2:
-                return True
+            if df.loc[index_maybe, column_name] > output_value:
+                close_value = df.loc[index_maybe, close_column]
+                # print(f"current value: {df.loc[index_maybe, column_name]} - close value {close_value}")
+                if close_value > value1 and close_value > value2:
+                    return True
+                break
         else:
-            if close_value < value1 and close_value < value2:
-                return True
-    else:
-        return False
+            if df.loc[index_maybe, column_name] < output_value:
+                close_value = df.loc[index_maybe, close_column]
+                if close_value < value1 and close_value < value2:
+                    return True
+                break
+    return False
+
+    # if is_high:
+    #     filtered_df = df[(df.index < max_index) & (df[column_name] > output_value)]
+    # else:
+    #     filtered_df = df[(df.index < max_index) & (df[column_name] < output_value)]
+    
+    # if not filtered_df.empty:
+    #     first_valid_row = filtered_df.iloc[0]
+
+    #     print(f"Respective close row: {first_valid_row}")
+    #     close_value = first_valid_row[close_column]
+    #     print(f"Respective close value: {close_value}")
+
+    #     if is_high:
+    #         if close_value > value1 and close_value > value2:
+    #             return True
+    #     else:
+    #         if close_value < value1 and close_value < value2:
+    #             return True
+    # else:
+    #     return False
     
 def get_output_value(value1, value2, is_high=False):
     if is_high:
@@ -91,7 +112,7 @@ def process_csv(file):
         DATE_NAME = "Date"
     
     # Add three empty columns between LOW PRICE and CLOSE PRICE
-    low_price_index = df.columns.get_loc(LOW_NAME)
+    low_price_index = df.columns.get_loc(CLOSE_NAME)
     df.insert(low_price_index + 1, 'HIGH Result', '')
     df.insert(low_price_index + 2, 'LOW Result', '')
     df.insert(low_price_index + 3, 'Empty Column', '')
@@ -114,7 +135,7 @@ def process_csv(file):
         # Insert an empty row
         df = pd.concat([df.iloc[:idx], pd.DataFrame([{'Separator': 'Separator'}]), df.iloc[idx:]]).reset_index(drop=True)
 
-    price_columns = [HIGH_NAME, LOW_NAME]
+    price_columns = [HIGH_NAME, LOW_NAME, CLOSE_NAME]
     df[price_columns] = df[price_columns].replace({',': ''}, regex=True).apply(pd.to_numeric, errors='coerce')
 
     # Calculate global thresholds for HIGH PRICE and LOW PRICE columns
@@ -167,10 +188,11 @@ def process_csv(file):
     df[DATE_NAME] = df[DATE_NAME].dt.strftime('%d-%b-%Y')
     # df['Last_Thursday'] = df['Last_Thursday'].dt.strftime('%d-%b-%Y')
 
-    styled_df = df.style.apply(lambda _: style_df, axis=None)
 
+    styled_df = df.style.apply(lambda _: style_df, axis=None)
     styled_df.to_excel(output_file, engine='openpyxl', index=False)
     
+    
     return output_file
 
 # Gradio Interface