Upload folder using huggingface_hub

Dockerfile

@@ -1,8 +1,10 @@
-
 FROM python:3.10-slim
+
 WORKDIR /app
 COPY requirements.txt .
 RUN pip install --no-cache-dir -r requirements.txt
+
 COPY app.py .
+
 EXPOSE 7860
 CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

@@ -1,6 +1,18 @@
+---
+---
+title: Predictive Maintenance
+emoji: 🔧
+colorFrom: blue
+colorTo: green
+sdk: docker
+python_version: "3.10"
+app_file: app.py
+pinned: false
+---
 
-# Predictive Maintenance - Engine Failure Prediction
+## Predictive Maintenance – Engine Failure Prediction
 
-Streamlit application deployed on Hugging Face Spaces.
+Streamlit app deployed on **Hugging Face Spaces** using Docker.
 
 Author: Sharley Kulkarni
+

app.py

@@ -1,7 +1,5 @@
-
 import streamlit as st
 import pandas as pd
-import numpy as np
 import joblib
 from huggingface_hub import hf_hub_download
 
@@ -36,9 +34,9 @@ if st.button("Predict"):
     }])
 
     X_scaled = scaler.transform(X)
-    prediction = model.predict(X_scaled)[0]
+    pred = model.predict(X_scaled)[0]
 
-    if prediction == 1:
+    if pred == 1:
         st.error("Engine Failure Detected")
     else:
         st.success("Engine Operating Normally")

deploy.py

@@ -0,0 +1,30 @@
+from huggingface_hub import HfApi, login
+import os
+
+HF_USERNAME = "SharleyK"
+SPACE_NAME = "PredictiveMaintenance"
+HF_TOKEN = os.getenv("HF_TOKEN")
+
+if not HF_TOKEN:
+    raise RuntimeError("HF_TOKEN environment variable not set")
+
+login(token=HF_TOKEN)
+api = HfApi()
+
+repo_id = f"{HF_USERNAME}/{SPACE_NAME}"
+
+api.create_repo(
+    repo_id=repo_id,
+    repo_type="space",
+    space_sdk="docker",
+    exist_ok=True
+)
+
+api.upload_folder(
+    folder_path=".",
+    repo_id=repo_id,
+    repo_type="space"
+)
+
+print("Deployment successful!")
+print(f"https://huggingface.co/spaces/{HF_USERNAME}/{SPACE_NAME}")

requirements.txt

@@ -1,8 +1,6 @@
-
 streamlit
 pandas
 numpy
 scikit-learn
 joblib
 huggingface-hub
-sdk: docker