adding api

app.py

@@ -2,6 +2,7 @@ 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)
@@ -51,10 +52,45 @@ def get_data():
     df["created_at"] = pd.to_datetime(df["created_at"], unit="s")
     return df
 
-# --- UI ---
-st.title(T[LANG]["title"])
 df = get_data()
 
+# --- API mode handling ---
+params = st.experimental_get_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) ---
+st.title(T[LANG]["title"])
+
 entity_counts = df["entity"].value_counts()
 domain_counts = df["domain"].value_counts()
 
@@ -120,8 +156,6 @@ 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])
@@ -149,6 +183,7 @@ 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"])
@@ -170,8 +205,6 @@ 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])
@@ -185,7 +218,6 @@ 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({