Upload folder using huggingface_hub

backend/database.py

@@ -1,5 +1,6 @@
 import sqlite3
 import os
+import time
 from datetime import datetime
 from huggingface_hub import HfApi, hf_hub_download
 
@@ -15,18 +16,26 @@ def sync_from_hub():
         print("No HF_TOKEN found. Skipping sync.")
         return
     try:
+        # Download with cache-busting logic if possible or just fresh download
         print(f"Downloading {DB_NAME} from Hub...")
-        path = hf_hub_download(repo_id=REPO_ID, filename=DB_NAME, repo_type="dataset", token=HF_TOKEN)
+        path = hf_hub_download(
+            repo_id=REPO_ID, 
+            filename=DB_NAME, 
+            repo_type="dataset", 
+            token=HF_TOKEN,
+            force_download=True
+        )
         import shutil
         shutil.copy(path, DB_PATH)
         print("Sync from Hub complete.")
     except Exception as e:
-        print(f"Sync from Hub failed (maybe first run?): {e}")
+        print(f"Sync from Hub failed: {e}")
 
 def sync_to_hub():
     if not HF_TOKEN:
         return
     try:
+        # Uploading back to Hub centralizes it for other instances
         api.upload_file(
             path_or_fileobj=DB_PATH,
             path_in_repo=DB_NAME,
@@ -59,6 +68,9 @@ def init_db():
     conn.close()
 
 def log_prediction(data, prediction_label, confidence):
+    # Pull latest before writing to avoid overwriting others' work
+    sync_from_hub()
+    
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
     cursor.execute('''
@@ -77,9 +89,14 @@ def log_prediction(data, prediction_label, confidence):
     ))
     conn.commit()
     conn.close()
+    
+    # Push immediately
     sync_to_hub()
 
 def get_history():
+    # Fresh pull for history view
+    sync_from_hub()
+    
     if not os.path.exists(DB_PATH):
         return []
     conn = sqlite3.connect(DB_PATH)

frontend/Home.py

@@ -5,6 +5,7 @@ import time
 import pandas as pd
 import plotly.graph_objects as go
 import numpy as np
+from datetime import timedelta
 
 API_URL = os.getenv("API_URL", "http://localhost:8000")
 if "hf.space" in os.getenv("SPACE_ID", ""):
@@ -124,7 +125,13 @@ def _candle_at(t_epoch, prev_alerts, curr_alerts):
     l = min(o, c) - rng.uniform(0.5, spread * 0.6)
     h = min(h, ALERT_MAX + 5)
     l = max(l, 0)
-    return {"time": pd.Timestamp.fromtimestamp(t_epoch), "open": round(o, 1), "high": round(h, 1), "low": round(l, 1), "close": round(c, 1)}
+    
+    # Convert UTC timestamp to IST for display
+    ts = pd.Timestamp.fromtimestamp(t_epoch)
+    if "hf.space" in os.getenv("SPACE_ID", ""):
+        ts = ts + timedelta(hours=5, minutes=30)
+        
+    return {"time": ts, "open": round(o, 1), "high": round(h, 1), "low": round(l, 1), "close": round(c, 1)}
 
 # ──────── Initialize: pre-fill chart as if running since startup ────────
 if "chart_candles" not in st.session_state: