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Fixed: write_file() now checks for empty directory path.
"""
import os, subprocess, sys, textwrap
def sh(cmd, check=True):
"""Helper to run shell commands."""
print(f"\nRUN: {cmd}\n")
result = subprocess.run(cmd, shell=True, capture_output=True, text=True)
print(result.stdout)
if check and result.returncode != 0:
print(result.stderr)
raise RuntimeError(f"Command failed: {cmd}")
return result
def write_file(path, content):
"""Helper to safely write a file, even in the current directory."""
directory = os.path.dirname(path)
if directory: # only create dirs if there's a parent path
os.makedirs(directory, exist_ok=True)
with open(path, "w") as f:
f.write(textwrap.dedent(content))
print(f"WROTE: {path}")
def main():
print("🚀 Starting automated deployment setup...")
github_repo = os.getenv("GITHUB_REPO")
github_token = os.getenv("GITHUB_TOKEN")
hf_token = os.getenv("HF_TOKEN")
hf_space = os.getenv("HF_SPACE_REPO")
if not github_repo or not github_token:
print("ERROR: Please set GITHUB_REPO and GITHUB_TOKEN environment variables.")
sys.exit(1)
# Clone or reuse repo
if not os.path.exists("engine-condition-predictor"):
sh(f"git clone https://{github_token}@github.com/{github_repo}.git engine-condition-predictor")
else:
print("✅ Repo already exists locally, skipping clone.")
os.chdir("engine-condition-predictor")
# Create files
write_file("Dockerfile", """
FROM python:3.10-slim
WORKDIR /app
RUN apt-get update && apt-get install -y --no-install-recommends build-essential git libgomp1 && rm -rf /var/lib/apt/lists/*
COPY requirements.txt .
RUN pip install --upgrade pip && pip install -r requirements.txt
COPY . .
EXPOSE 7860
CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]
""")
write_file("requirements.txt", """
streamlit
pandas
numpy
scikit-learn
xgboost
joblib
huggingface-hub
""")
write_file("app.py", """
import streamlit as st
import pandas as pd
import numpy as np
import joblib
from huggingface_hub import hf_hub_download
import os
st.title("⚙️ Predictive Maintenance: Engine Condition Predictor")
st.write("Upload data or input manually to predict engine condition using XGBoost model.")
# Constants for model/scaler paths on Hugging Face Hub
HF_TOKEN = os.getenv("HF_TOKEN") # Get token from environment variables (Colab secrets or GitHub secrets)
MODEL_REPO_ID = "sriharimudakavi/engine-condition-xgboost-tuned"
MODEL_FILENAME = "xgboost_tuned_model.joblib"
SCALER_REPO_ID = "sriharimudakavi/engine-data" # Assuming scaler is in the dataset repo
SCALER_FILENAME = "scaler.joblib"
# Download model and scaler
try:
model_path = hf_hub_download(repo_id=MODEL_REPO_ID, filename=MODEL_FILENAME, repo_type="model", token=HF_TOKEN)
scaler_path = hf_hub_download(repo_id=SCALER_REPO_ID, filename=SCALER_FILENAME, repo_type="dataset", token=HF_TOKEN)
except Exception as e:
st.error(f"Error downloading model or scaler: {e}")
st.stop()
# Load model and scaler
model = joblib.load(model_path)
scaler = joblib.load(scaler_path)
option = st.sidebar.selectbox("Input Method", ["Manual Entry", "Upload CSV"])
if option == "Manual Entry":
rpm = st.number_input("Engine RPM", 0, 3000, 800)
oil_p = st.number_input("Lube Oil Pressure", 0.0, 10.0, 3.0)
fuel_p = st.number_input("Fuel Pressure", 0.0, 25.0, 6.0)
cool_p = st.number_input("Coolant Pressure", 0.0, 10.0, 2.0)
oil_t = st.number_input("Lube Oil Temp (°C)", 60.0, 120.0, 80.0)
cool_t = st.number_input("Coolant Temp (°C)", 60.0, 200.0, 90.0)
input_df = pd.DataFrame([[rpm, oil_p, fuel_p, cool_p, oil_t, cool_t]],
columns=["Engine rpm", "Lub oil pressure", "Fuel pressure", "Coolant pressure", "lub oil temp", "Coolant temp"])
st.write(input_df)
if st.button("🔍 Predict Engine Condition"):
# Scale the input data
scaled_input_df = scaler.transform(input_df)
pred = model.predict(scaled_input_df)[0]
st.success(f"Predicted Condition: {'Normal (0)' if pred==0 else 'Faulty (1)'}")
else:
file = st.file_uploader("Upload CSV file", type=["csv"])
if file:
input_df = pd.read_csv(file)
st.write("Uploaded Data:")
st.dataframe(input_df)
if st.button("🔍 Predict Engine Condition from CSV"):
# Ensure the columns match the training data
if not all(col in input_df.columns for col in ["Engine rpm", "Lub oil pressure", "Fuel pressure", "Coolant pressure", "lub oil temp", "Coolant temp"]):
st.error("CSV file must contain 'Engine rpm', 'Lub oil pressure', 'Fuel pressure', 'Coolant pressure', 'lub oil temp', 'Coolant temp' columns.")
else:
# Scale the input data
scaled_input_df = scaler.transform(input_df[["Engine rpm", "Lub oil pressure", "Fuel pressure", "Coolant pressure", "lub oil temp", "Coolant temp"]])
preds = model.predict(scaled_input_df)
input_df["Predicted Condition"] = np.where(preds==0, "Normal (0)", "Faulty (1)")
st.write("Predictions:")
st.dataframe(input_df)
""")
write_file(".github/workflows/pipeline.yml", """
name: ML Deployment Pipeline
on:
push:
branches: [ main ]
jobs:
deploy:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Set up Python
uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install Dependencies
run: |
pip install -r requirements.txt
- name: Deploy to Hugging Face
env:
HF_TOKEN: ${{ secrets.HF_TOKEN }}
run: |
python host_to_hf.py
""")
write_file("host_to_hf.py", """
from huggingface_hub import HfApi
import os
HF_TOKEN = os.getenv("HF_TOKEN")
REPO_ID = os.getenv("HF_SPACE_REPO", "sriharimudakavi/engine-condition-predictor") # Use env var or default
api = HfApi()
api.upload_folder(folder_path=".", repo_id=REPO_ID, repo_type="space", token=HF_TOKEN)
print("✅ Uploaded to Hugging Face Space successfully.")
""")
# Git commit and push
sh("git add .")
sh('git config user.name "sriharimudakavi5"')
sh('git config user.email "sriharimudakavi5@gmail.com"')
sh('git commit -m "Fix app.py: direct model/scaler loading and remove self-update logic" || echo "No changes to commit"')
sh("git push origin main")
print("✅ Deployment files pushed to GitHub successfully.")
if __name__ == "__main__":
main()
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