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@@ -913,8 +913,7 @@ with tab_explore:
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for col in df_display.columns:
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if 'image' in col.lower():
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df_display[col] = df_display[col].apply(lambda x: x if is_valid_image_url(x) else "")
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- # Replace None values with
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- df_display.replace(to_replace=r'^None$', value=' ', regex=True, inplace=True)
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+ df_display.replace(to_replace=[None, 'None', 'nan', 'NaN'], value=' ', inplace=True)
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# Only fillna with empty string on object columns to avoid Arrow float64 conversion errors
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for col in df_display.columns:
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if df_display[col].dtype == 'object':
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@@ -1189,7 +1188,7 @@ with tab_plate:
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"Nutrition Image": r.get('image_nutrition_small_url') if is_valid_image_url(r.get('image_nutrition_small_url')) else "",
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})
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gallery_df = pd.DataFrame(df_rows)
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- gallery_df.replace(to_replace=r'^None$', value=' ', regex=True, inplace=True)
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+ gallery_df.replace(to_replace=[None, 'None', 'nan', 'NaN'], value=' ', inplace=True)
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gallery_df.index = range(1, len(gallery_df) + 1)
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st.dataframe(
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gallery_df,
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@@ -1331,8 +1330,8 @@ with tab_planner:
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if any(w in cols[0].lower() or w in cols[1].lower() for w in ['total', 'summation', 'global']):
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continue
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def clean_num(val):
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- m = re.search(r'([0-9]+(?:\.[0-9]+)?)', val)
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- return float(m.group(1)) if m else 0.0
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+ nums = re.findall(r'([0-9]+(?:\.[0-9]+)?)', val)
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+ return float(nums[-1]) if nums else 0.0
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total_cal += clean_num(cols[3])
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total_pro += clean_num(cols[4])
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total_carb += clean_num(cols[5])
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