cloud version

.github/workflows/sync_to_hf.yml

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+name: Sync to Hugging Face Hub
+on:
+  push:
+    branches: [main]
+
+  # Allows you to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+        with:
+          fetch-depth: 0
+          lfs: true
+      
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        # The target is your specific space URL with the token injected
+        run: git push --force https://ashandilgith:$HF_TOKEN@huggingface.co/spaces/ashandilgith/piranaware_version3 main

.gitignore

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 gcp_key.json
 __pycache__/
-*.pyc
+*.pyctemp_audio_uploads/

Dockerfile

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+# Use Python 3.9 (Stable for TensorFlow)
+FROM python:3.9-slim
+
+# Set working directory
+WORKDIR /app
+
+# Install audio libraries required by Librosa
+RUN apt-get update && apt-get install -y \
+    libsndfile1 \
+    ffmpeg \
+    && rm -rf /var/lib/apt/lists/*
+
+# Copy your files
+COPY . .
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Run Streamlit on Port 7860
+CMD ["streamlit", "run", "app.py", "--server.port=7860", "--server.address=0.0.0.0"]

README.md

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+# 🌊 Piranaware: Acoustic Digital Twin for Marine Engines
+
+**Predictive Maintenance / Anomaly Detection System v1.0**
+*Listen to what your engine is telling you—before it breaks.*
+
+---
+
+## 📖 Overview
+Piranaware is an AI-powered diagnostic tool designed for marine vessels. It creates a "Digital Twin" of an engine's acoustic signature using Deep Learning (Autoencoders). By comparing current engine sounds against a healthy baseline, it can detect mechanical degradation (bearing wear, valve drift, misfires) weeks before catastrophic failure.
+
+**Deployed on:** Hugging Face Spaces (Docker)
+**Backend:** Google Cloud Platform (Storage)
+**ML Engine:** TensorFlow / Keras
+
+---
+
+## 🚀 Key Features
+* **Tri-Sonic Calibration:** Trains distinct models for **IDLE**, **SLOW**, and **FAST** RPM states.
+* **Cloud Sync:** Automatically uploads trained models to secure Google Cloud Storage buckets.
+* **Statistical Thresholding:** Uses dynamic safety limits (`Mean + 2σ`) to filter out false positives.
+* **Mobile First:** Responsive Streamlit interface designed for use in engine rooms.
+
+---
+
+## 🛠️ Tech Stack
+* **Frontend:** Streamlit
+* **ML Core:** TensorFlow, Librosa (Audio Processing), NumPy
+* **Infrastructure:** Docker, Google Cloud Storage (GCS)
+* **CI/CD:** GitHub Actions -> Hugging Face Hub
+
+---
+
+## ⚙️ Setup & Installation
+
+### Prerequisites
+* Python 3.9+
+* FFmpeg (for audio processing)
+* Google Cloud Service Account Key
+
+### 1. Local Development
+```bash
+# Clone the repo
+git clone [https://github.com/your-username/piranaware.git](https://github.com/your-username/piranaware.git)
+cd piranaware
+
+# Create virtual environment
+python -m venv venv
+source venv/bin/activate  # On Windows: venv\Scripts\activate
+
+# Install dependencies
+pip install -r requirements.txt
+
+# Run the app
+streamlit run app.py