Initial Deployment

.github/workflows/sync_to_hub.yml

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+name: Sync to Hugging Face hub
+on:
+  push:
+    branches: [main]
+
+  # 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: ${{ secrets.HF_TOKEN }}
+        run: git push https://NurseCitizenDeveloper:$HF_TOKEN@huggingface.co/spaces/NurseCitizenDeveloper/VirtualGeneScope main

README.md

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+# Virtual Gene Scope
+
+A nursing education tool to visualize the "Regulatory Genome" using Google DeepMind's AlphaGenome (Mocked/API).
+
+## Overview
+This application demonstrates how non-coding variants can impact gene expression, helping nurses understand complex genetic conditions beyond simple protein-coding errors.
+
+## Features
+- **Gene Selector**: Inspect key genes (e.g., *INS*, *SCN9A*).
+- **Mutation Simulator**: Introduce variants in regulatory regions.
+- **Visual Tracks**: See predicted changes in Promoter Activity and Splicing.
+
+## Usage
+1. Run locally:
+   ```bash
+   pip install -r requirements.txt
+   streamlit run app.py
+   ```
+2. Deploy to Hugging Face Spaces (CPU Basic).

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+import matplotlib.pyplot as plt
+
+# Page Config
+st.set_page_config(
+    page_title="Virtual Gene Scope",
+    page_icon="🧬",
+    layout="wide"
+)
+
+# Custom CSS for Nursing/Medical Aesthetic
+st.markdown("""
+    <style>
+    .main {
+        background-color: #f8f9fa;
+    }
+    h1 {
+        color: #2c3e50;
+    }
+    .stButton>button {
+        background-color: #007bff;
+        color: white;
+    }
+    .metric-card {
+        background-color: white;
+        padding: 20px;
+        border-radius: 10px;
+        box-shadow: 0 4px 6px rgba(0,0,0,0.1);
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+# Application Header
+st.title("🧬 Virtual Gene Scope: The Regulatory Genome")
+st.markdown("""
+**Nursing Context**: 98% of the genome does not code for proteins. Instead, it acts as a "control panel" turning genes on and off.
+**AlphaGenome** allows us to see this hidden layer. Use this tool to visualize how non-coding mutations affects patient health.
+""")
+
+# Sidebar controls
+st.sidebar.header("Patient & Gene Settings")
+gene_choice = st.sidebar.selectbox("Select Gene of Interest", ["INS (Insulin - Diabetes)", "SCN9A (Pain Sensitivity)", "MMP9 (Wound Healing)"])
+mutation_intensity = st.sidebar.slider("Mutation Impact (Simulation)", 0.0, 1.0, 0.5, help="Simulate the severity of the regulatory disruption.")
+
+# Mock Data Generation (Simulating AlphaGenome Output)
+def get_mock_tracks(gene, mutation_factor):
+    # Base signal (Promoter activity)
+    x = np.linspace(0, 100, 500)
+    
+    # "Normal" gene expression peak
+    base_signal = np.exp(-((x - 50)**2) / 20) 
+    
+    # "Mutated" signal - reduced by mutation factor
+    mutated_signal = base_signal * (1 - mutation_factor * 0.8) # up to 80% reduction
+    
+    return x, base_signal, mutated_signal
+
+# Main Content Area
+col1, col2 = st.columns([1, 2])
+
+with col1:
+    st.subheader("Variant details")
+    st.info(f"Analyzing Regulatory Region for **{gene_choice.split(' ')[0]}**")
+    
+    st.write("Variant Type: **Non-Coding / Regulatory**")
+    st.write("Location: **Promoter / Enhancer Region**")
+    
+    if mutation_intensity > 0.7:
+        st.error("⚠️ HIGH RISK: Significant reduction in gene expression predicted.")
+    elif mutation_intensity > 0.3:
+        st.warning("⚠️ MODERATE RISK: Partial loss of regulation.")
+    else:
+        st.success("✅ LOW RISK: Benign variant predicted.")
+
+with col2:
+    st.subheader("AlphaGenome Predicted Activity Tracks")
+    
+    x, normal, mutant = get_mock_tracks(gene_choice, mutation_intensity)
+    
+    fig, ax = plt.subplots(figsize=(10, 5))
+    ax.plot(x, normal, label="Healthy Control (Reference)", color='green', linewidth=2, alpha=0.7)
+    ax.plot(x, mutant, label="Patient Variant (Alternative)", color='red', linewidth=2, linestyle='--')
+    
+    ax.set_title(f"Promoter Activity: {gene_choice.split(' ')[0]}", fontsize=12)
+    ax.set_xlabel("Genomic Position (bp)")
+    ax.set_ylabel("Predicted RNA Expression")
+    ax.legend()
+    ax.grid(True, alpha=0.3)
+    ax.fill_between(x, normal, mutant, color='orange', alpha=0.2, label='Lost Expression')
+    
+    st.pyplot(fig)
+
+st.markdown("---")
+st.caption("Powered by AlphaGenome Architecture (Mock Demo) | Nursing Genomics Education Toolkit")

requirements.txt

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+streamlit
+pandas
+matplotlib
+numpy
+huggingface_hub
+# alphagenome - install from git in runtime or use mock for now