reverting api

app.py

@@ -2,7 +2,6 @@ import streamlit as st
 import pandas as pd
 import plotly.express as px
 from sqlalchemy import create_engine, text
-from urllib.parse import unquote
 
 # --- Language toggle ---
 LANG = st.radio("Language / Език", ["English", "Български"], horizontal=True)
@@ -52,44 +51,9 @@ def get_data():
     df["created_at"] = pd.to_datetime(df["created_at"], unit="s")
     return df
 
-df = get_data()
-
-# --- API mode handling ---
-params = st.query_params
-route = params.get("api", [None])[0]
-
-if route == "meta":
-    st.json({
-        "entities": sorted(df["entity"].dropna().unique().tolist()),
-        "domains": sorted(df["domain"].dropna().unique().tolist()),
-        "score_types": ["entity_score", "title_score", "overall_score"],
-        "min_date": df["created_at"].min().isoformat(),
-        "max_date": df["created_at"].max().isoformat(),
-    })
-    st.stop()
-
-elif route == "data":
-    entity = unquote(params.get("entity", [""])[0])
-    domain = unquote(params.get("domain", [""])[0])
-    score = params.get("score", ["entity_score"])[0]
-
-    df_filtered = df.copy()
-    if entity:
-        df_filtered = df_filtered[df_filtered["entity"] == entity]
-    if domain:
-        df_filtered = df_filtered[df_filtered["domain"] == domain]
-
-    if score not in ["entity_score", "title_score", "overall_score"]:
-        st.json({"error": "Invalid score type"})
-    else:
-        df_out = df_filtered[["created_at", "entity", "domain", score]].copy()
-        df_out["created_at"] = df_out["created_at"].astype(str)
-        df_out.rename(columns={score: "score"}, inplace=True)
-        st.json(df_out.to_dict(orient="records"))
-    st.stop()
-
-# --- UI (non-API) ---
+# --- UI ---
 st.title(T[LANG]["title"])
+df = get_data()
 
 entity_counts = df["entity"].value_counts()
 domain_counts = df["domain"].value_counts()
@@ -156,6 +120,8 @@ if not filtered_df.empty:
             return label
 
         grouped["label"] = grouped.apply(build_label, axis=1)
+
+        # ✅ Sort by avg score then by date
         label_avg = grouped.groupby("label")["score"].mean().reset_index(name="avg_score")
         grouped = grouped.merge(label_avg, on="label")
         grouped = grouped.sort_values(by=["avg_score", "date"], ascending=[False, True])
@@ -183,7 +149,6 @@ if not filtered_df.empty:
         stats_group = ["entity"]
         if group_by_domain:
             stats_group.append("domain")
-
         stats = (
             filtered_df.groupby(stats_group)[score_type]
             .agg(["mean", "count"])
@@ -205,6 +170,8 @@ if not filtered_df.empty:
             return label
 
         grouped["label"] = grouped.apply(build_label, axis=1)
+
+        # ✅ Sort by avg score then by date
         label_avg = grouped.groupby("label")[score_type].mean().reset_index(name="avg_score")
         grouped = grouped.merge(label_avg, on="label")
         grouped = grouped.sort_values(by=["avg_score", "date"], ascending=[False, True])
@@ -218,6 +185,7 @@ if not filtered_df.empty:
             title=T[LANG]["avg_over_time"].format(score_type.replace("_", " ").title())
         )
 
+    # Grid lines at every integer, thicker at -5 and 5
     shapes = []
     for y in range(-10, 11):
         shapes.append({