Upload folder using huggingface_hub

database.py

@@ -1,10 +1,44 @@
 import sqlite3
 import os
 from datetime import datetime
+from huggingface_hub import HfApi, hf_hub_download
 
-DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'patient_history.db')
+REPO_ID = "IDKHowToCodeFr/tinyml-logs"
+DB_NAME = 'patient_history.db'
+DB_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), DB_NAME)
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+api = HfApi()
+
+def sync_from_hub():
+    if not HF_TOKEN:
+        print("No HF_TOKEN found. Skipping sync.")
+        return
+    try:
+        print(f"Downloading {DB_NAME} from Hub...")
+        path = hf_hub_download(repo_id=REPO_ID, filename=DB_NAME, repo_type="dataset", token=HF_TOKEN)
+        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}")
+
+def sync_to_hub():
+    if not HF_TOKEN:
+        return
+    try:
+        api.upload_file(
+            path_or_fileobj=DB_PATH,
+            path_in_repo=DB_NAME,
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+    except Exception as e:
+        print(f"Sync to Hub failed: {e}")
 
 def init_db():
+    sync_from_hub()
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
     cursor.execute('''
@@ -43,23 +77,21 @@ def log_prediction(data, prediction_label, confidence):
     ))
     conn.commit()
     conn.close()
+    sync_to_hub()
 
 def get_history():
+    if not os.path.exists(DB_PATH):
+        return []
     conn = sqlite3.connect(DB_PATH)
     cursor = conn.cursor()
     cursor.execute('SELECT * FROM predictions ORDER BY timestamp DESC LIMIT 100')
     rows = cursor.fetchall()
-    
-    # Get column names
     col_names = [description[0] for description in cursor.description]
-    
     conn.close()
     
-    # Format as list of dicts
     history = []
     for row in rows:
         history.append(dict(zip(col_names, row)))
-        
     return history
 
 # Initialize db when this module is loaded

main.py

@@ -99,6 +99,21 @@ async def predict(data: PatientData):
 def history():
     return get_history()
 
+@app.get("/dataset")
+async def get_dataset():
+    data_path = '/app/data/patient_dataset.csv' if os.path.exists('/app/data') else '../data/patient_dataset.csv' if os.path.exists('../data') else 'data/patient_dataset.csv'
+    if os.path.exists(data_path):
+        df = pd.read_csv(data_path)
+        # Return summary or partial data to avoid huge payloads
+        return df.to_dict(orient="records")
+    return {"error": "Dataset not found"}
+
+@app.get("/sync")
+def force_sync():
+    from database import sync_from_hub, sync_to_hub
+    sync_from_hub()
+    return {"status": "Sync attempted"}
+
 @app.post("/explain")
 async def explain(data: PatientData):
     eng = get_ensemble()

requirements.txt

@@ -8,3 +8,4 @@ shap
 m2cgen
 matplotlib
 python-multipart
+huggingface_hub