Delete data_processing

data_processing/__init__.py

@@ -1,44 +0,0 @@
-# __init__.py
-
-# Imports from posts_categorization module
-from .posts_categorization import (
-    summarize_post,
-    classify_post,
-    summarize_and_classify_post,
-    batch_summarize_and_classify,
-    SummaryOutput,
-    ClassificationOutput,
-    CLASSIFICATION_LABELS,
-    PRIMARY_SUMMARIZER_MODEL,
-    FALLBACK_SUMMARIZER_MODEL,
-    CLASSIFICATION_MODEL
-)
-
-# Imports from analytics_data_processing module
-from .analytics_data_processing import (
-    filter_dataframe_by_date,
-    prepare_filtered_analytics_data,
-    generate_chatbot_data_summaries
-)
-
-# Define __all__ to specify what is exported when 'from package import *' is used
-__all__ = [
-    # From posts_categorization
-    'summarize_post',
-    'classify_post',
-    'summarize_and_classify_post',
-    'batch_summarize_and_classify',
-    'SummaryOutput',
-    'ClassificationOutput',
-    'CLASSIFICATION_LABELS',
-    'PRIMARY_SUMMARIZER_MODEL',
-    'FALLBACK_SUMMARIZER_MODEL',
-    'CLASSIFICATION_MODEL',
-
-    # From analytics_data_processing
-    'filter_dataframe_by_date',
-    'prepare_filtered_analytics_data',
-    'generate_chatbot_data_summaries'
-]
-
-

data_processing/analytics_data_processing.py

@@ -1,503 +0,0 @@
-#analytics_data_processing.py
-import pandas as pd
-from datetime import datetime, timedelta, time
-import logging
-import numpy as np
-
-# Configure logging for this module
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(module)s - %(message)s')
-
-def filter_dataframe_by_date(df, date_column, start_date, end_date):
-    """Filters a DataFrame by a date column within a given date range."""
-    if df is None or df.empty or not date_column:
-        logging.warning(f"Filter by date: DataFrame is None, empty, or no date_column provided. DF: {df is not None}, empty: {df.empty if df is not None else 'N/A'}, date_column: {date_column}")
-        return pd.DataFrame()
-    if date_column not in df.columns:
-        logging.warning(f"Filter by date: Date column '{date_column}' not found in DataFrame columns: {df.columns.tolist()}.")
-        return pd.DataFrame()
-    
-    df_copy = df.copy() # Work on a copy to avoid SettingWithCopyWarning
-    try:
-        # Ensure the date column is pandas datetime objects
-        if not pd.api.types.is_datetime64_any_dtype(df_copy[date_column]):
-            df_copy[date_column] = pd.to_datetime(df_copy[date_column], errors='coerce')
-
-        # Drop rows where date conversion might have failed (NaT) or was originally NaT
-        df_copy.dropna(subset=[date_column], inplace=True)
-        if df_copy.empty:
-            logging.info(f"Filter by date: DataFrame empty after to_datetime and dropna for column '{date_column}'.")
-            return pd.DataFrame()
-
-        # Normalize to midnight. This preserves timezone information if present.
-        df_copy[date_column] = df_copy[date_column].dt.normalize()
-
-        # If the column is timezone-aware, convert its values to naive UTC equivalent.
-        # This allows comparison with naive filter dates.
-        if hasattr(df_copy[date_column].dt, 'tz') and df_copy[date_column].dt.tz is not None:
-            logging.info(f"Column '{date_column}' is timezone-aware ({df_copy[date_column].dt.tz}). Converting to naive (from UTC) for comparison.")
-            df_copy[date_column] = df_copy[date_column].dt.tz_convert('UTC').dt.tz_localize(None)
-
-    except Exception as e:
-        logging.error(f"Error processing date column '{date_column}': {e}", exc_info=True)
-        return pd.DataFrame() 
-
-    # Convert start_date and end_date (which are naive Python datetime or naive Pandas Timestamp)
-    # to naive pandas Timestamps and normalize them.
-    start_dt_obj = pd.to_datetime(start_date, errors='coerce').normalize() if start_date else None
-    end_dt_obj = pd.to_datetime(end_date, errors='coerce').normalize() if end_date else None
-
-    # Perform the filtering
-    # df_filtered is already df_copy which has NaNs dropped and dates processed
-    if start_dt_obj and end_dt_obj:
-        df_filtered_final = df_copy[(df_copy[date_column] >= start_dt_obj) & (df_copy[date_column] <= end_dt_obj)]
-    elif start_dt_obj:
-        df_filtered_final = df_copy[df_copy[date_column] >= start_dt_obj]
-    elif end_dt_obj:
-        df_filtered_final = df_copy[df_copy[date_column] <= end_dt_obj]
-    else:
-        df_filtered_final = df_copy # No date filtering if neither start_date nor end_date is provided
-    
-    if df_filtered_final.empty:
-        logging.info(f"Filter by date: DataFrame became empty after applying date range to column '{date_column}'.")
-    
-    return df_filtered_final
-
-
-def prepare_filtered_analytics_data(token_state_value, date_filter_option, custom_start_date, custom_end_date):
-    """
-    Retrieves data from token_state, determines date range, filters posts, mentions, and follower time-series data.
-    Merges posts with post stats.
-    Returns:
-        - filtered_merged_posts_df: Posts merged with stats, filtered by date.
-        - filtered_mentions_df: Mentions filtered by date.
-        - date_filtered_follower_stats_df: Follower stats filtered by date (for time-series plots).
-        - raw_follower_stats_df: Unfiltered follower stats (for demographic plots).
-        - start_dt_filter: Determined start date for filtering.
-        - end_dt_filter: Determined end date for filtering.
-    """
-    logging.info(f"Preparing filtered analytics data. Filter: {date_filter_option}, Custom Start: {custom_start_date}, Custom End: {custom_end_date}")
-
-    posts_df = token_state_value.get("bubble_posts_df", pd.DataFrame()).copy()
-    mentions_df = token_state_value.get("bubble_mentions_df", pd.DataFrame()).copy()
-    follower_stats_df = token_state_value.get("bubble_follower_stats_df", pd.DataFrame()).copy()
-    post_stats_df = token_state_value.get("bubble_post_stats_df", pd.DataFrame()).copy() # Fetch post_stats_df
-
-    date_column_posts = token_state_value.get("config_date_col_posts", "published_at")
-    date_column_mentions = token_state_value.get("config_date_col_mentions", "date")
-    # Assuming follower_stats_df has a 'date' column for time-series data
-    date_column_followers = token_state_value.get("config_date_col_followers", "date") 
-
-    # Determine date range for filtering
-    current_datetime_obj = datetime.now()
-    current_time_normalized = current_datetime_obj.replace(hour=0, minute=0, second=0, microsecond=0) 
-    
-    end_dt_filter = current_time_normalized 
-    start_dt_filter = None
-
-    if date_filter_option == "Last 7 Days":
-        start_dt_filter = current_time_normalized - timedelta(days=6) 
-    elif date_filter_option == "Last 30 Days":
-        start_dt_filter = current_time_normalized - timedelta(days=29) 
-    elif date_filter_option == "Custom Range":
-        start_dt_filter_temp = pd.to_datetime(custom_start_date, errors='coerce')
-        start_dt_filter = start_dt_filter_temp.replace(hour=0, minute=0, second=0, microsecond=0) if pd.notna(start_dt_filter_temp) else None 
-        
-        end_dt_filter_temp = pd.to_datetime(custom_end_date, errors='coerce')
-        end_dt_filter = end_dt_filter_temp.replace(hour=0, minute=0, second=0, microsecond=0) if pd.notna(end_dt_filter_temp) else current_time_normalized
-    
-    logging.info(f"Date range for filtering: Start: {start_dt_filter}, End: {end_dt_filter}")
-
-    # Merge posts_df and post_stats_df
-    merged_posts_df = pd.DataFrame()
-    if not posts_df.empty and not post_stats_df.empty:
-        # Assuming posts_df has 'id' and post_stats_df has 'post_id' for merging
-        if 'id' in posts_df.columns and 'post_id' in post_stats_df.columns:
-            merged_posts_df = pd.merge(posts_df, post_stats_df, left_on='id', right_on='post_id', how='left')
-            logging.info(f"Merged posts_df ({len(posts_df)} rows) and post_stats_df ({len(post_stats_df)} rows) into merged_posts_df ({len(merged_posts_df)} rows).")
-        else:
-            logging.warning("Cannot merge posts_df and post_stats_df due to missing 'id' or 'post_id' columns.")
-            # Fallback to using posts_df if merge fails but provide an empty df for stats-dependent plots
-            merged_posts_df = posts_df # Or handle as an error / empty DF for those plots
-    elif not posts_df.empty:
-        logging.warning("post_stats_df is empty. Proceeding with posts_df only for plots that don't require stats.")
-        merged_posts_df = posts_df # Create necessary columns with NaN if they are expected by plots
-        # For columns expected from post_stats_df, add them with NaNs if not present
-        expected_stat_cols = ['engagement', 'impressionCount', 'clickCount', 'likeCount', 'commentCount', 'shareCount']
-        for col in expected_stat_cols:
-            if col not in merged_posts_df.columns:
-                merged_posts_df[col] = pd.NA
-
-
-    # Filter DataFrames by date
-    filtered_merged_posts_data = pd.DataFrame()
-    if not merged_posts_df.empty and date_column_posts in merged_posts_df.columns:
-        filtered_merged_posts_data = filter_dataframe_by_date(merged_posts_df, date_column_posts, start_dt_filter, end_dt_filter)
-    elif not merged_posts_df.empty:
-        logging.warning(f"Date column '{date_column_posts}' not found in merged_posts_df. Returning unfiltered merged posts data.")
-        filtered_merged_posts_data = merged_posts_df # Or apply other logic
-    
-    filtered_mentions_data = pd.DataFrame()
-    if not mentions_df.empty and date_column_mentions in mentions_df.columns:
-        filtered_mentions_data = filter_dataframe_by_date(mentions_df, date_column_mentions, start_dt_filter, end_dt_filter)
-    elif not mentions_df.empty:
-        logging.warning(f"Date column '{date_column_mentions}' not found in mentions_df. Returning unfiltered mentions data.")
-        filtered_mentions_data = mentions_df
-
-    date_filtered_follower_stats_df = pd.DataFrame()
-    raw_follower_stats_df = follower_stats_df.copy() # For demographic plots, use raw (or latest snapshot logic)
-
-    if not follower_stats_df.empty and date_column_followers in follower_stats_df.columns:
-        date_filtered_follower_stats_df = filter_dataframe_by_date(follower_stats_df, date_column_followers, start_dt_filter, end_dt_filter)
-    elif not follower_stats_df.empty:
-        logging.warning(f"Date column '{date_column_followers}' not found in follower_stats_df. Time-series follower plots might be empty or use unfiltered data.")
-        # Decide if date_filtered_follower_stats_df should be raw_follower_stats_df or empty
-        date_filtered_follower_stats_df = follower_stats_df # Or pd.DataFrame() if strict filtering is required
-
-    logging.info(f"Processed - Filtered Merged Posts: {len(filtered_merged_posts_data)} rows, Filtered Mentions: {len(filtered_mentions_data)} rows, Date-Filtered Follower Stats: {len(date_filtered_follower_stats_df)} rows.")
-    
-    return filtered_merged_posts_data, filtered_mentions_data, date_filtered_follower_stats_df, raw_follower_stats_df, start_dt_filter, end_dt_filter
-
-# --- Helper function to generate textual data summaries for chatbot ---
-def generate_chatbot_data_summaries(
-    plot_configs_list,
-    filtered_merged_posts_df,
-    filtered_mentions_df,
-    date_filtered_follower_stats_df, # Expected to contain 'follower_gains_monthly'
-    raw_follower_stats_df,          # Expected to contain other demographics like 'follower_geo', 'follower_industry'
-    token_state_value
-    ):
-    """
-    Generates textual summaries for each plot ID to be used by the chatbot,
-    based on the corrected understanding of DataFrame structures and follower count columns.
-    """
-    data_summaries = {}
-    
-    # --- Date and Config Columns from token_state ---
-    # For Posts
-    date_col_posts = token_state_value.get("config_date_col_posts", "published_at")
-    media_type_col_name = token_state_value.get("config_media_type_col", "media_type")
-    eb_labels_col_name = token_state_value.get("config_eb_labels_col", "li_eb_label")
-    # For Mentions
-    date_col_mentions = token_state_value.get("config_date_col_mentions", "date")
-    mentions_sentiment_col = "sentiment_label" # As per user's mention df structure
-    
-    # For Follower Stats - Actual column names provided by user
-    follower_count_organic_col = "follower_count_organic"
-    follower_count_paid_col = "follower_count_paid"
-
-    # For Follower Stats (Demographics from raw_follower_stats_df)
-    follower_demographics_type_col = "follower_count_type" # Column indicating 'follower_geo', 'follower_industry'
-    follower_demographics_category_col = "category_name"   # Column indicating 'USA', 'Technology'
-
-    # For Follower Gains/Growth (from date_filtered_follower_stats_df)
-    follower_gains_type_col = "follower_count_type" # Should be 'follower_gains_monthly'
-    follower_gains_date_col = "category_name"       # This is 'YYYY-MM-DD'
-
-    # --- Helper: Safely convert to datetime ---
-    def safe_to_datetime(series, errors='coerce'):
-        return pd.to_datetime(series, errors=errors)
-
-    # --- Prepare DataFrames (copy and convert dates) ---
-    if filtered_merged_posts_df is not None and not filtered_merged_posts_df.empty:
-        posts_df = filtered_merged_posts_df.copy()
-        if date_col_posts in posts_df.columns:
-            posts_df[date_col_posts] = safe_to_datetime(posts_df[date_col_posts])
-        else:
-            logging.warning(f"Date column '{date_col_posts}' not found in posts_df for chatbot summary.")
-    else:
-        posts_df = pd.DataFrame()
-
-    if filtered_mentions_df is not None and not filtered_mentions_df.empty:
-        mentions_df = filtered_mentions_df.copy()
-        if date_col_mentions in mentions_df.columns:
-            mentions_df[date_col_mentions] = safe_to_datetime(mentions_df[date_col_mentions])
-        else:
-            logging.warning(f"Date column '{date_col_mentions}' not found in mentions_df for chatbot summary.")
-    else:
-        mentions_df = pd.DataFrame()
-
-    # For date_filtered_follower_stats_df (monthly gains)
-    if date_filtered_follower_stats_df is not None and not date_filtered_follower_stats_df.empty:
-        follower_monthly_df = date_filtered_follower_stats_df.copy()
-        if follower_gains_type_col in follower_monthly_df.columns:
-             follower_monthly_df = follower_monthly_df[follower_monthly_df[follower_gains_type_col] == 'follower_gains_monthly'].copy()
-        
-        if follower_gains_date_col in follower_monthly_df.columns:
-            follower_monthly_df['datetime_obj'] = safe_to_datetime(follower_monthly_df[follower_gains_date_col])
-            follower_monthly_df = follower_monthly_df.dropna(subset=['datetime_obj'])
-            
-            # Calculate total gains
-            if follower_count_organic_col in follower_monthly_df.columns and follower_count_paid_col in follower_monthly_df.columns:
-                follower_monthly_df[follower_count_organic_col] = pd.to_numeric(follower_monthly_df[follower_count_organic_col], errors='coerce').fillna(0)
-                follower_monthly_df[follower_count_paid_col] = pd.to_numeric(follower_monthly_df[follower_count_paid_col], errors='coerce').fillna(0)
-                follower_monthly_df['total_monthly_gains'] = follower_monthly_df[follower_count_organic_col] + follower_monthly_df[follower_count_paid_col]
-            elif follower_count_organic_col in follower_monthly_df.columns: # Only organic exists
-                 follower_monthly_df[follower_count_organic_col] = pd.to_numeric(follower_monthly_df[follower_count_organic_col], errors='coerce').fillna(0)
-                 follower_monthly_df['total_monthly_gains'] = follower_monthly_df[follower_count_organic_col]
-            elif follower_count_paid_col in follower_monthly_df.columns: # Only paid exists
-                 follower_monthly_df[follower_count_paid_col] = pd.to_numeric(follower_monthly_df[follower_count_paid_col], errors='coerce').fillna(0)
-                 follower_monthly_df['total_monthly_gains'] = follower_monthly_df[follower_count_paid_col]
-            else:
-                logging.warning(f"Neither '{follower_count_organic_col}' nor '{follower_count_paid_col}' found in follower_monthly_df for total gains calculation.")
-                follower_monthly_df['total_monthly_gains'] = 0 # Avoid KeyError later
-        else:
-            logging.warning(f"Date column '{follower_gains_date_col}' (from category_name) not found in follower_monthly_df for chatbot summary.")
-            if 'datetime_obj' not in follower_monthly_df.columns:
-                 follower_monthly_df['datetime_obj'] = pd.NaT 
-            if 'total_monthly_gains' not in follower_monthly_df.columns:
-                 follower_monthly_df['total_monthly_gains'] = 0
-    else:
-        follower_monthly_df = pd.DataFrame(columns=[follower_gains_date_col, 'total_monthly_gains', 'datetime_obj'])
-
-
-    if raw_follower_stats_df is not None and not raw_follower_stats_df.empty:
-        follower_demographics_df = raw_follower_stats_df.copy()
-        # Calculate total followers for demographics
-        if follower_count_organic_col in follower_demographics_df.columns and follower_count_paid_col in follower_demographics_df.columns:
-            follower_demographics_df[follower_count_organic_col] = pd.to_numeric(follower_demographics_df[follower_count_organic_col], errors='coerce').fillna(0)
-            follower_demographics_df[follower_count_paid_col] = pd.to_numeric(follower_demographics_df[follower_count_paid_col], errors='coerce').fillna(0)
-            follower_demographics_df['total_follower_count'] = follower_demographics_df[follower_count_organic_col] + follower_demographics_df[follower_count_paid_col]
-        elif follower_count_organic_col in follower_demographics_df.columns:
-            follower_demographics_df[follower_count_organic_col] = pd.to_numeric(follower_demographics_df[follower_count_organic_col], errors='coerce').fillna(0)
-            follower_demographics_df['total_follower_count'] = follower_demographics_df[follower_count_organic_col]
-        elif follower_count_paid_col in follower_demographics_df.columns:
-            follower_demographics_df[follower_count_paid_col] = pd.to_numeric(follower_demographics_df[follower_count_paid_col], errors='coerce').fillna(0)
-            follower_demographics_df['total_follower_count'] = follower_demographics_df[follower_count_paid_col]
-        else:
-            logging.warning(f"Neither '{follower_count_organic_col}' nor '{follower_count_paid_col}' found in follower_demographics_df for total count calculation.")
-            if 'total_follower_count' not in follower_demographics_df.columns:
-                 follower_demographics_df['total_follower_count'] = 0
-    else:
-        follower_demographics_df = pd.DataFrame()
-
-
-    for plot_cfg in plot_configs_list:
-        plot_id = plot_cfg["id"]
-        plot_label = plot_cfg["label"]
-        summary_text = f"No specific data summary available for '{plot_label}' for the selected period."
-
-        try:
-            # --- FOLLOWER STATS ---
-            if plot_id == "followers_count": # Uses follower_monthly_df
-                if not follower_monthly_df.empty and 'total_monthly_gains' in follower_monthly_df.columns and 'datetime_obj' in follower_monthly_df.columns and not follower_monthly_df['datetime_obj'].isnull().all():
-                    df_summary = follower_monthly_df[['datetime_obj', 'total_monthly_gains']].copy()
-                    df_summary['datetime_obj'] = df_summary['datetime_obj'].dt.strftime('%Y-%m-%d')
-                    df_summary.rename(columns={'datetime_obj': 'Date', 'total_monthly_gains': 'Total Monthly Gains'}, inplace=True)
-                    summary_text = f"Follower Count (Total Monthly Gains):\n{df_summary.sort_values(by='Date').tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Follower count data (total monthly gains) is unavailable or incomplete for '{plot_label}'."
-            
-            elif plot_id == "followers_growth_rate": # Uses follower_monthly_df
-                if not follower_monthly_df.empty and 'total_monthly_gains' in follower_monthly_df.columns and 'datetime_obj' in follower_monthly_df.columns and not follower_monthly_df['datetime_obj'].isnull().all():
-                    df_calc = follower_monthly_df.sort_values(by='datetime_obj').copy()
-                    # Growth rate is calculated on the total monthly gains (which are changes, not cumulative counts)
-                    # To calculate growth rate of followers, we'd need cumulative follower count.
-                    # The plot logic also uses pct_change on the gains themselves.
-                    # If 'total_monthly_gains' represents the *change* in followers, then pct_change on this is rate of change of gains.
-                    # If it represents the *cumulative* followers at that point, then pct_change is follower growth rate.
-                    # Assuming 'total_monthly_gains' is the *change* for the month, like the plot logic.
-                    df_calc['total_monthly_gains'] = pd.to_numeric(df_calc['total_monthly_gains'], errors='coerce')
-                    if len(df_calc) >= 2:
-                        # Calculate cumulative sum to get follower count if 'total_monthly_gains' are indeed just gains
-                        # If your 'total_monthly_gains' already IS the total follower count at end of month, remove next line
-                        # For now, assuming it's GAINS, so we need cumulative for growth rate of total followers.
-                        # However, the original plot logic applies pct_change directly to 'follower_gains_monthly'.
-                        # Let's stick to pct_change on the gains/count column for consistency with plot.
-                        
-                        # If 'total_monthly_gains' is the actual follower count for that month:
-                        df_calc['growth_rate_monthly'] = df_calc['total_monthly_gains'].pct_change() * 100
-                        df_calc['growth_rate_monthly'] = df_calc['growth_rate_monthly'].round(2)
-                        df_calc.replace([np.inf, -np.inf], np.nan, inplace=True) # Handle division by zero if a gain was 0
-                        
-                        df_summary = df_calc[['datetime_obj', 'growth_rate_monthly']].dropna().copy()
-                        df_summary['datetime_obj'] = df_summary['datetime_obj'].dt.strftime('%Y-%m-%d')
-                        df_summary.rename(columns={'datetime_obj': 'Date', 'growth_rate_monthly': 'Growth Rate (%)'}, inplace=True)
-                        if not df_summary.empty:
-                            summary_text = f"Follower Growth Rate (Monthly % based on Total Follower Count/Gains):\n{df_summary.sort_values(by='Date').tail(5).to_string(index=False)}"
-                        else:
-                            summary_text = f"Not enough data points or valid transitions to calculate follower growth rate for '{plot_label}'."
-                    else:
-                        summary_text = f"Not enough data points (need at least 2) to calculate follower growth rate for '{plot_label}'."
-                else:
-                    summary_text = f"Follower growth rate data (total monthly gains) is unavailable or incomplete for '{plot_label}'."
-
-            elif plot_id in ["followers_by_location", "followers_by_role", "followers_by_industry", "followers_by_seniority"]:
-                demographic_type_map = {
-                    "followers_by_location": "follower_geo",
-                    "followers_by_role": "follower_function",
-                    "followers_by_industry": "follower_industry",
-                    "followers_by_seniority": "follower_seniority"
-                }
-                current_demographic_type = demographic_type_map.get(plot_id)
-                if not follower_demographics_df.empty and \
-                   follower_demographics_type_col in follower_demographics_df.columns and \
-                   follower_demographics_category_col in follower_demographics_df.columns and \
-                   'total_follower_count' in follower_demographics_df.columns: # Check for the calculated total
-                    
-                    df_filtered_demographics = follower_demographics_df[
-                        follower_demographics_df[follower_demographics_type_col] == current_demographic_type
-                    ].copy()
-
-                    if not df_filtered_demographics.empty:
-                        df_summary = df_filtered_demographics.groupby(follower_demographics_category_col)['total_follower_count'].sum().reset_index()
-                        df_summary.rename(columns={follower_demographics_category_col: 'Category', 'total_follower_count': 'Total Follower Count'}, inplace=True)
-                        top_5 = df_summary.nlargest(5, 'Total Follower Count')
-                        summary_text = f"Top 5 {plot_label} (Total Followers):\n{top_5.to_string(index=False)}"
-                    else:
-                        summary_text = f"No data available for demographic type '{current_demographic_type}' in '{plot_label}'."
-                else:
-                    summary_text = f"Follower demographic data columns (including total_follower_count) are missing or incomplete for '{plot_label}'."
-
-            # --- POSTS STATS ---
-            elif plot_id == "engagement_rate":
-                if not posts_df.empty and 'engagement' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['engagement'].resample('W').mean().reset_index()
-                    df_resampled['engagement'] = pd.to_numeric(df_resampled['engagement'], errors='coerce').round(2)
-                    df_summary = df_resampled[[date_col_posts, 'engagement']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    summary_text = f"Engagement Rate Over Time (Weekly Avg %):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Engagement rate data is unavailable for '{plot_label}'."
-            
-            elif plot_id == "reach_over_time": 
-                if not posts_df.empty and 'reach' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['reach'].resample('W').sum().reset_index()
-                    df_resampled['reach'] = pd.to_numeric(df_resampled['reach'], errors='coerce')
-                    df_summary = df_resampled[[date_col_posts, 'reach']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    summary_text = f"Reach Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Reach data is unavailable for '{plot_label}'."
-
-            elif plot_id == "impressions_over_time": 
-                if not posts_df.empty and 'impressionCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['impressionCount'].resample('W').sum().reset_index()
-                    df_resampled['impressionCount'] = pd.to_numeric(df_resampled['impressionCount'], errors='coerce')
-                    df_summary = df_resampled[[date_col_posts, 'impressionCount']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    df_summary.rename(columns={'impressionCount': 'Impressions'}, inplace=True)
-                    summary_text = f"Impressions Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Impressions data is unavailable for '{plot_label}'."
-
-            elif plot_id == "likes_over_time": 
-                if not posts_df.empty and 'likeCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['likeCount'].resample('W').sum().reset_index()
-                    df_resampled['likeCount'] = pd.to_numeric(df_resampled['likeCount'], errors='coerce')
-                    df_summary = df_resampled[[date_col_posts, 'likeCount']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    df_summary.rename(columns={'likeCount': 'Likes'}, inplace=True)
-                    summary_text = f"Likes Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Likes data is unavailable for '{plot_label}'."
-
-            elif plot_id == "clicks_over_time": 
-                if not posts_df.empty and 'clickCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['clickCount'].resample('W').sum().reset_index()
-                    df_resampled['clickCount'] = pd.to_numeric(df_resampled['clickCount'], errors='coerce')
-                    df_summary = df_resampled[[date_col_posts, 'clickCount']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    df_summary.rename(columns={'clickCount': 'Clicks'}, inplace=True)
-                    summary_text = f"Clicks Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Clicks data is unavailable for '{plot_label}'."
-
-            elif plot_id == "shares_over_time": 
-                if not posts_df.empty and 'shareCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['shareCount'].resample('W').sum().reset_index()
-                    df_resampled['shareCount'] = pd.to_numeric(df_resampled['shareCount'], errors='coerce')
-                    df_summary = df_resampled[[date_col_posts, 'shareCount']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    df_summary.rename(columns={'shareCount': 'Shares'}, inplace=True)
-                    summary_text = f"Shares Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                elif 'shareCount' not in posts_df.columns and not posts_df.empty : # Check if posts_df is not empty before assuming column is the only issue
-                     summary_text = f"Shares data column ('shareCount') not found for '{plot_label}'."
-                else:
-                    summary_text = f"Shares data is unavailable for '{plot_label}'."
-            
-            elif plot_id == "comments_over_time": 
-                if not posts_df.empty and 'commentCount' in posts_df.columns and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    df_resampled = posts_df.set_index(date_col_posts)['commentCount'].resample('W').sum().reset_index()
-                    df_resampled['commentCount'] = pd.to_numeric(df_resampled['commentCount'], errors='coerce')
-                    df_summary = df_resampled[[date_col_posts, 'commentCount']].dropna().copy()
-                    df_summary[date_col_posts] = df_summary[date_col_posts].dt.strftime('%Y-%m-%d')
-                    df_summary.rename(columns={'commentCount': 'Comments'}, inplace=True)
-                    summary_text = f"Comments Over Time (Weekly Sum):\n{df_summary.sort_values(by=date_col_posts).tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Comments data is unavailable for '{plot_label}'."
-
-            elif plot_id == "comments_sentiment": 
-                comment_sentiment_col_posts = "sentiment" 
-                if not posts_df.empty and comment_sentiment_col_posts in posts_df.columns:
-                    sentiment_counts = posts_df[comment_sentiment_col_posts].value_counts().reset_index()
-                    sentiment_counts.columns = ['Sentiment', 'Count']
-                    summary_text = f"Comments Sentiment Breakdown (Posts Data):\n{sentiment_counts.to_string(index=False)}"
-                else:
-                    summary_text = f"Comment sentiment data ('{comment_sentiment_col_posts}') is unavailable for '{plot_label}'."
-            
-            elif plot_id == "post_frequency_cs":
-                if not posts_df.empty and date_col_posts in posts_df.columns and not posts_df[date_col_posts].isnull().all():
-                    post_counts_weekly = posts_df.set_index(date_col_posts).resample('W').size().reset_index(name='post_count')
-                    post_counts_weekly.rename(columns={date_col_posts: 'Week', 'post_count': 'Posts'}, inplace=True)
-                    post_counts_weekly['Week'] = post_counts_weekly['Week'].dt.strftime('%Y-%m-%d (Week of)')
-                    summary_text = f"Post Frequency (Weekly):\n{post_counts_weekly.sort_values(by='Week').tail(5).to_string(index=False)}"
-                else:
-                    summary_text = f"Post frequency data is unavailable for '{plot_label}'."
-
-            elif plot_id == "content_format_breakdown_cs":
-                if not posts_df.empty and media_type_col_name in posts_df.columns:
-                    format_counts = posts_df[media_type_col_name].value_counts().reset_index()
-                    format_counts.columns = ['Format', 'Count']
-                    summary_text = f"Content Format Breakdown:\n{format_counts.nlargest(5, 'Count').to_string(index=False)}"
-                else:
-                    summary_text = f"Content format data ('{media_type_col_name}') is unavailable for '{plot_label}'."
-
-            elif plot_id == "content_topic_breakdown_cs":
-                if not posts_df.empty and eb_labels_col_name in posts_df.columns:
-                    try:
-                        # Ensure the column is not all NaN before trying to check for lists or explode
-                        if posts_df[eb_labels_col_name].notna().any():
-                            if posts_df[eb_labels_col_name].apply(lambda x: isinstance(x, list)).any():
-                                topic_counts = posts_df.explode(eb_labels_col_name)[eb_labels_col_name].value_counts().reset_index()
-                            else: 
-                                topic_counts = posts_df[eb_labels_col_name].value_counts().reset_index()
-                            topic_counts.columns = ['Topic', 'Count']
-                            summary_text = f"Content Topic Breakdown (Top 5):\n{topic_counts.nlargest(5, 'Count').to_string(index=False)}"
-                        else:
-                            summary_text = f"Content topic data ('{eb_labels_col_name}') contains no valid topics for '{plot_label}'."
-                    except Exception as e_topic:
-                        logging.warning(f"Could not process topic breakdown for '{eb_labels_col_name}': {e_topic}")
-                        summary_text = f"Content topic data ('{eb_labels_col_name}') could not be processed for '{plot_label}'."
-                else:
-                    summary_text = f"Content topic data ('{eb_labels_col_name}') is unavailable for '{plot_label}'."
-
-            # --- MENTIONS STATS ---
-            elif plot_id == "mention_analysis_volume": 
-                if not mentions_df.empty and date_col_mentions in mentions_df.columns and not mentions_df[date_col_mentions].isnull().all():
-                    mentions_over_time = mentions_df.set_index(date_col_mentions).resample('W').size().reset_index(name='mention_count')
-                    mentions_over_time.rename(columns={date_col_mentions: 'Week', 'mention_count': 'Mentions'}, inplace=True)
-                    mentions_over_time['Week'] = mentions_over_time['Week'].dt.strftime('%Y-%m-%d (Week of)')
-                    if not mentions_over_time.empty:
-                        summary_text = f"Mentions Volume (Weekly):\n{mentions_over_time.sort_values(by='Week').tail(5).to_string(index=False)}"
-                    else:
-                        summary_text = f"No mention activity found for '{plot_label}' in the selected period."
-                else:
-                    summary_text = f"Mentions volume data is unavailable for '{plot_label}'."
-            
-            elif plot_id == "mention_analysis_sentiment": 
-                if not mentions_df.empty and mentions_sentiment_col in mentions_df.columns:
-                    sentiment_counts = mentions_df[mentions_sentiment_col].value_counts().reset_index()
-                    sentiment_counts.columns = ['Sentiment', 'Count']
-                    summary_text = f"Mentions Sentiment Breakdown:\n{sentiment_counts.to_string(index=False)}"
-                else:
-                    summary_text = f"Mention sentiment data ('{mentions_sentiment_col}') is unavailable for '{plot_label}'."
-
-            data_summaries[plot_id] = summary_text
-        except KeyError as e:
-            logging.warning(f"KeyError generating summary for {plot_id} ('{plot_label}'): {e}. Using default summary.")
-            data_summaries[plot_id] = f"Data summary generation error for '{plot_label}' (missing column: {e})."
-        except Exception as e:
-            logging.error(f"Error generating summary for {plot_id} ('{plot_label}'): {e}", exc_info=True)
-            data_summaries[plot_id] = f"Error generating data summary for '{plot_label}'."
-            
-    return data_summaries

data_processing/posts_categorization.py

@@ -1,207 +0,0 @@
-import pandas as pd
-from groq import Groq, RateLimitError
-import instructor
-from pydantic import BaseModel
-import os
-
-# Ensure GROQ_API_KEY is set in your environment variables
-api_key = os.getenv('GROQ_API_KEY')
-
-if not api_key:
-    raise ValueError("GROQ_API_KEY environment variable not set.")
-
-# Create single patched Groq client with instructor for structured output
-# Using Mode.JSON for structured output based on Pydantic models
-client = instructor.from_groq(Groq(api_key=api_key), mode=instructor.Mode.JSON)
-
-# Pydantic model for summarization output
-class SummaryOutput(BaseModel):
-    summary: str
-
-# Pydantic model for classification output
-class ClassificationOutput(BaseModel):
-    category: str
-
-# Define model names (as per your original code)
-PRIMARY_SUMMARIZER_MODEL = "deepseek-r1-distill-llama-70b" 
-FALLBACK_SUMMARIZER_MODEL = "llama-3.3-70b-versatile"
-CLASSIFICATION_MODEL = "meta-llama/llama-4-maverick-17b-128e-instruct" # Or your preferred classification model
-
-# Define the standard list of categories, including "None"
-CLASSIFICATION_LABELS = [
-    "Company Culture and Values",
-    "Employee Stories and Spotlights",
-    "Work-Life Balance, Flexibility, and Well-being",
-    "Diversity, Equity, and Inclusion (DEI)",
-    "Professional Development and Growth Opportunities",
-    "Mission, Vision, and Social Responsibility",
-    "None"  # Represents no applicable category or cases where classification isn't possible
-]
-
-def summarize_post(text: str) -> str | None:
-    """
-    Summarizes the given post text using a primary model with a fallback.
-    Returns the summary string or None if summarization fails or input is invalid.
-    """
-    # Check for NaN, None, or empty/whitespace-only string
-    if pd.isna(text) or text is None or not str(text).strip():
-        print("Summarizer: Input text is empty or None. Returning None.")
-        return None
-
-    # Truncate text to a reasonable length to avoid token overflow and reduce costs
-    processed_text = str(text)[:500] 
-
-    prompt = f"""
-    Summarize the following LinkedIn post in 5 to 10 words.
-    Only return the summary inside a JSON field called 'summary'.
-
-    Post Text:
-    \"\"\"{processed_text}\"\"\"
-    """
-
-    try:
-        # Attempt with primary model
-        print(f"Attempting summarization with primary model: {PRIMARY_SUMMARIZER_MODEL}")
-        response = client.chat.completions.create(
-            model=PRIMARY_SUMMARIZER_MODEL,
-            response_model=SummaryOutput,
-            messages=[
-                {"role": "system", "content": "You are a precise summarizer. Only return a JSON object with a 'summary' string."},
-                {"role": "user", "content": prompt}
-            ],
-            temperature=0.3
-        )
-        return response.summary
-    except RateLimitError:
-        print(f"Rate limit hit for primary summarizer model: {PRIMARY_SUMMARIZER_MODEL}. Trying fallback: {FALLBACK_SUMMARIZER_MODEL}")
-        try:
-            # Attempt with fallback model
-            response = client.chat.completions.create(
-                model=FALLBACK_SUMMARIZER_MODEL,
-                response_model=SummaryOutput,
-                messages=[
-                    {"role": "system", "content": "You are a precise summarizer. Only return a JSON object with a 'summary' string."},
-                    {"role": "user", "content": prompt}
-                ],
-                temperature=0.3 
-            )
-            print(f"Summarization successful with fallback model: {FALLBACK_SUMMARIZER_MODEL}")
-            return response.summary
-        except RateLimitError as rle_fallback:
-            print(f"Rate limit hit for fallback summarizer model ({FALLBACK_SUMMARIZER_MODEL}): {rle_fallback}. Summarization failed.")
-            return None
-        except Exception as e_fallback:
-            print(f"Error during summarization with fallback model ({FALLBACK_SUMMARIZER_MODEL}): {e_fallback}")
-            return None
-    except Exception as e_primary:
-        print(f"Error during summarization with primary model ({PRIMARY_SUMMARIZER_MODEL}): {e_primary}")
-        # Consider if fallback should be attempted for other errors too, or just return None
-        return None
-
-def classify_post(summary: str | None, labels: list[str]) -> str:
-    """
-    Classifies the post summary into one of the provided labels.
-    Ensures the returned category is one of the labels, defaulting to "None".
-    """
-    # If the summary is None (e.g., from a failed summarization or empty input),
-    # or if the summary is an empty string after stripping, classify as "None".
-    if pd.isna(summary) or summary is None or not str(summary).strip():
-        print("Classifier: Input summary is empty or None. Returning 'None' category.")
-        return "None" # Return the string "None" to match the label
-
-    # Join labels for the prompt to ensure the LLM knows the exact expected strings
-    labels_string = "', '".join(labels)
-
-    prompt = f"""
-    Post Summary: "{summary}"
-
-    Available Categories:
-    '{labels_string}'
-
-    Task: Choose the single most relevant category from the list above that applies to this summary.
-    Return ONLY ONE category string in a structured JSON format under the field 'category'.
-    The category MUST be one of the following: '{labels_string}'.
-    If no specific category applies, or if you are unsure, return "None".
-    """
-    try:
-        system_message = (
-            f"You are a very strict classifier. Your ONLY job is to return a JSON object "
-            f"with a 'category' field. The value of 'category' MUST be one of these "
-            f"exact strings: '{labels_string}'."
-        )
-        result = client.chat.completions.create(
-            model=CLASSIFICATION_MODEL,
-            response_model=ClassificationOutput,
-            messages=[
-                {"role": "system", "content": system_message},
-                {"role": "user", "content": prompt}
-            ],
-            temperature=0 # Temperature 0 for deterministic classification
-        )
-        
-        returned_category = result.category
-        
-        # Validate the output against the provided labels
-        if returned_category not in labels:
-            print(f"Warning: Classifier returned '{returned_category}', which is not in the predefined labels. Forcing to 'None'. Summary: '{summary}'")
-            return "None" # Force to "None" if the LLM returns an unexpected category
-        return returned_category
-    except Exception as e:
-        print(f"Classification error: {e}. Summary: '{summary}'. Defaulting to 'None' category.")
-        return "None" # Default to "None" on any exception during classification
-
-def summarize_and_classify_post(text: str | None, labels: list[str]) -> dict:
-    """
-    Summarizes and then classifies a single post text.
-    Handles cases where text is None or summarization fails.
-    """
-    summary = summarize_post(text) # This can return None
-    
-    # If summarization didn't produce a result (e.g. empty input, error),
-    # or if the summary itself is effectively empty, the category is "None".
-    if summary is None or not summary.strip():
-        category = "None" 
-    else:
-        # If we have a valid summary, try to classify it.
-        # classify_post is designed to return one of the labels or "None".
-        category = classify_post(summary, labels)
-        
-    return {
-        "summary": summary,  # This can be None
-        "category": category # This will be one of the labels or "None"
-    }
-
-def batch_summarize_and_classify(posts_data: list[dict]) -> list[dict]:
-    """
-    Processes a batch of posts, performing summarization and classification for each.
-    Expects posts_data to be a list of dictionaries, each with at least 'id' and 'text' keys.
-    Returns a list of dictionaries, each with 'id', 'summary', and 'category'.
-    """
-    
-    results = []
-    if not posts_data:
-        print("Input 'posts_data' is empty. Returning empty results.")
-        return results
-
-    for i, post_item in enumerate(posts_data):
-        if not isinstance(post_item, dict):
-            print(f"Warning: Item at index {i} is not a dictionary. Skipping.")
-            continue
-            
-        post_id = post_item.get("id")
-        text_to_process = post_item.get("text") # This text is passed to summarize_and_classify_post
-
-        print(f"\nProcessing Post ID: {post_id if post_id else 'N/A (ID missing)'}, Text: '{str(text_to_process)[:50]}...'")
-        
-        # summarize_and_classify_post will handle None/empty text internally
-        # and ensure category is "None" in such cases.
-        summary_and_category_result = summarize_and_classify_post(text_to_process, CLASSIFICATION_LABELS)
-
-        results.append({
-            "id": post_id, # Include the ID for mapping back to original data
-            "summary": summary_and_category_result["summary"],
-            "category": summary_and_category_result["category"] # This is now validated
-        })
-        print(f"Result for Post ID {post_id}: Summary='{summary_and_category_result['summary']}', Category='{summary_and_category_result['category']}'")
-        
-    return results