Update app.py

app.py

@@ -1,52 +1,7 @@
-import streamlit as st
-import pandas as pd
-import plotly.express as px
-import duckdb
-import os
-
-# --- Set page layout ---
-st.set_page_config(layout="wide", page_title="Sentiment Analysis")
-
-# --- CSS for Flag Toggle ---
-st.markdown("""
-    <style>
-    .flag-container { display: flex; justify-content: center; gap: 10px; }
-    .flag { cursor: pointer; width: 40px; height: 30px; }
-    </style>
-""", unsafe_allow_html=True)
-
-# --- State & Language ---
-flag_selection = st.radio("", ["🇬🇧", "🇧🇬"], horizontal=True, label_visibility="collapsed")
-LANG = "English" if flag_selection == "🇬🇧" else "Български"
-
-T = {
-    "English": {
-        "title": "📊 Sentiment Analysis Dashboard",
-        "select_entities": "Select Entities",
-        "select_domains": "Select Domains",
-        "score_type": "Select Score Type",
-        "group_by_domain": "Group by Domain",
-        "all_scores": "Show All Score Types",
-        "no_data": "No data matches the selected filters.",
-        "avg_over_time": "Average {} Over Time",
-        "scores_over_time": "Sentiment Scores Over Time",
-        "select_entities_prompt": "Please select at least one entity."
-    },
-    "Български": {
-        "title": "📊 Табло за анализ на настроенията",
-        "select_entities": "Изберете обекти",
-        "select_domains": "Изберете източници",
-        "score_type": "Изберете тип оценка",
-        "group_by_domain": "Групирай по сайт",
-        "all_scores": "Покажи всички типове оценки",
-        "no_data": "Няма данни за избраните филтри.",
-        "avg_over_time": "Средна стойност на {} във времето",
-        "scores_over_time": "Оценки на настроенията във времето",
-        "select_entities_prompt": "Моля, изберете поне един обект."
-    }
 }
 
 # --- MotherDuck Connection ---
+
 @st.cache_resource
 def get_connection():
     try:
@@ -58,20 +13,17 @@ def get_connection():
 
 con = get_connection()
 
-# --- Metadata for Filters ---
+# --- Cached Metadata for Filters ---
 @st.cache_data(ttl=600)
 def get_filter_options():
-    # Trim and count for cleaner lookups
-    entities = con.sql("SELECT trim(entity) as entity, COUNT(*) as c FROM sentiment_analysis GROUP BY 1 ORDER BY c DESC").df()
-    domains = con.sql("SELECT trim(domain) as domain, COUNT(*) as c FROM sentiment_analysis GROUP BY 1 ORDER BY c DESC").df()
+    # Fast counts via DuckDB
+    entities = con.sql("SELECT entity, COUNT(*) as c FROM sentiment_analysis GROUP BY 1 ORDER BY c DESC").df()
+    domains = con.sql("SELECT domain, COUNT(*) as c FROM sentiment_analysis GROUP BY 1 ORDER BY c DESC").df()
     return entities, domains
-
-df_entities, df_domains = get_filter_options()
-
-# --- Sidebar ---
 st.title(T[LANG]["title"])
 
 with st.sidebar:
+    # Create lookup dictionaries to avoid filtering dataframes in the lambda (prevents crashes)
     entity_lookup = dict(zip(df_entities['entity'], df_entities['c']))
     domain_lookup = dict(zip(df_domains['domain'], df_domains['c']))
 
@@ -88,59 +40,32 @@ with st.sidebar:
     )
 
     score_type = st.selectbox(T[LANG]["score_type"], ["entity_score", "title_score", "overall_score"])
-    group_by_domain = st.checkbox(T[LANG]["group_by_domain"])
-    all_scores = st.checkbox(T[LANG]["all_scores"])
-    
-    timeframes = {"All": 9999, "Last 7 Days": 7, "Last 30 Days": 30, "Last Year": 365}
-    time_choice = st.selectbox("Timeframe", list(timeframes.keys()))
-
-# --- Logic: Stop if no selection ---
-if not selected_entities:
     st.info(T[LANG]["select_entities_prompt"])
     st.stop()
 
-# --- Dynamic Granularity Logic (Targeting ~50 points) ---
-days = timeframes[time_choice]
-if days <= 50:
-    bucket = "1 day"
-elif days <= 365:
-    bucket = "1 week"
-else:
-    bucket = "1 month"
-
-# --- Query Building ---
+# Dynamic SQL construction
 where_clause = f"WHERE entity IN ({str(selected_entities)[1:-1]})"
 if selected_domains:
     where_clause += f" AND domain IN ({str(selected_domains)[1:-1]})"
 if time_choice != "All":
-    where_clause += f" AND created_at >= (epoch(now()) - {days * 86400})"
+    where_clause += f" AND created_at >= (epoch(now()) - {timeframes[time_choice] * 86400})"
+
 
 if all_scores:
+
     sql_query = f"""
     SELECT 
-        time_bucket(interval '{bucket}', created_at) as date,
+        time_bucket(interval '1 day', created_at) as date,
         entity,
         domain,
         score_name as score_type,
-        AVG(score_value) as score
-    FROM (
-        UNPIVOT sentiment_analysis 
-        ON entity_score, title_score, overall_score
-        INTO NAME score_name VALUE score_value
-    )
-    {where_clause}
-    GROUP BY ALL
-    """
 else:
     sql_query = f"""
     SELECT 
-        time_bucket(interval '{bucket}', created_at) as date,
+        time_bucket(interval '1 day', created_at) as date,
         entity,
         domain,
         AVG({score_type}) as score
-    FROM sentiment_analysis
-    {where_clause}
-    GROUP BY ALL
     """
 
 # --- Execution & Plotting ---
@@ -153,34 +78,31 @@ except Exception as e:
 if filtered_df.empty:
     st.warning(T[LANG]["no_data"])
 else:
-    # Build legend labels
+    # Build dynamic labels for the legend
     color_col = "entity"
     if group_by_domain:
         filtered_df["label"] = filtered_df["entity"] + " | " + filtered_df["domain"]
         color_col = "label"
     
     if all_scores:
-        current_base = filtered_df["label"] if group_by_domain else filtered_df["entity"]
-        filtered_df["label"] = current_base + " | " + filtered_df["score_type"]
+        current_label = filtered_df["label"] if group_by_domain else filtered_df["entity"]
+        filtered_df["label"] = current_label + " | " + filtered_df["score_type"]
         color_col = "label"
 
-    # Plot
     fig = px.line(
         filtered_df.sort_values("date"),
         x="date", y="score", color=color_col,
-        title=f"{T[LANG]['scores_over_time']} (Grouped by {bucket})",
-        labels={"score": "Score", "date": "Date"}
+        title=T[LANG]["scores_over_time"] if all_scores else T[LANG]["avg_over_time"].format(score_type.replace('_', ' ').title()),
+        labels={"score": "Sentiment Score", "date": "Date", "label": "Entity/Source"}
     )
 
-    # Layout styling
+    # Styling
     fig.update_layout(
         yaxis=dict(range=[-10, 10], gridcolor="lightgrey"),
         plot_bgcolor="white",
         legend=dict(orientation="h", y=-0.2, x=0.5, xanchor="center")
     )
     
-    # Reference lines
+    # Static horizontal reference lines
     for val in [-5, 0, 5]:
-        fig.add_hline(y=val, line_width=2 if val==0 else 1, line_dash="dash", line_color="black")
-
-    st.plotly_chart(fig, use_container_width=True)
+        fig.add_hline(y=val, line_width=2 if val==0 else 1, line_dash="dash", line_color="black")