Upload data_app.py

data_app.py

@@ -2,11 +2,38 @@ import streamlit as st
 import pandas as pd
 from datetime import datetime
 import os
+from huggingface_hub import HfApi, snapshot_download
 
 st.set_page_config(page_title="🔧 Service Tech Tracker", layout="wide")
 st.title("🔧 Service Tech Fuel & Job Tracker")
 
-# Create folders
+# ====================== HF DATASET SETUP ======================
+HF_REPO = "AddisonSwan/service-tech-tracker"
+HF_TOKEN = st.secrets.get("HF_TOKEN")
+
+def load_mileage():
+    try:
+        # Try to download latest data
+        snapshot_download(repo_id=HF_REPO, repo_type="dataset", allow_patterns="mileage.csv", token=HF_TOKEN)
+        return pd.read_csv("mileage.csv")
+    except:
+        return pd.DataFrame(columns=["Date", "Country", "Region", "Price", "Miles", "Type", "Notes"])
+
+def save_mileage(df):
+    df.to_csv("mileage.csv", index=False)
+    try:
+        api = HfApi(token=HF_TOKEN)
+        api.upload_file(
+            path_or_fileobj="mileage.csv",
+            path_in_repo="mileage.csv",
+            repo_id=HF_REPO,
+            repo_type="dataset"
+        )
+        st.success("✅ Saved to Hugging Face Dataset")
+    except:
+        st.warning("Saved locally only")
+
+# Create local folders
 os.makedirs("data", exist_ok=True)
 os.makedirs("jobs", exist_ok=True)
 
@@ -23,11 +50,11 @@ if page == "Gas & Mileage":
     price = st.number_input("Gas Price", value=4.50, step=0.01)
     miles = st.number_input("Miles Driven", value=0.0)
     trip_type = st.radio("Type", ["Work", "Personal"])
-    notes = st.text_area("Notes (Job #, location, etc.)")
+    notes = st.text_area("Notes")
     
     if st.button("Save Entry"):
         new_row = pd.DataFrame([{
-            "Date": datetime.now().strftime("%Y-%m-%d"),
+            "Date": datetime.now().strftime("%Y-%m-%d %H:%M"),
             "Country": country,
             "Region": region,
             "Price": price,
@@ -36,16 +63,13 @@ if page == "Gas & Mileage":
             "Notes": notes
         }])
         
-        try:
-            df = pd.read_csv("data/mileage.csv")
-            df = pd.concat([df, new_row], ignore_index=True)
-        except:
-            df = new_row
-        df.to_csv("data/mileage.csv", index=False)
-        st.success("✅ Saved!")
-
-    if os.path.exists("data/mileage.csv"):
-        st.dataframe(pd.read_csv("data/mileage.csv"))
+        df = load_mileage()
+        df = pd.concat([df, new_row], ignore_index=True)
+        save_mileage(df)
+    
+    df = load_mileage()
+    if not df.empty:
+        st.dataframe(df)
 
 elif page == "New Job":
     st.subheader("📁 New Job")
@@ -56,9 +80,4 @@ elif page == "New Job":
         os.makedirs(f"jobs/{job_id}", exist_ok=True)
         st.success(f"Job {job_id} created!")
 
-elif page == "All Data":
-    st.subheader("All Records")
-    if os.path.exists("data/mileage.csv"):
-        st.dataframe(pd.read_csv("data/mileage.csv"))
-
-st.caption("Service Tech Tracker • Hugging Face")
+st.caption("Connected to Hugging Face Dataset • Private Cloud Backup")