Backup v1 20251110 0243

.dockerignore

@@ -0,0 +1,9 @@
+.venv
+__pycache__
+*.pyc
+.pytest_cache
+.git
+docs
+data/*.csv
+.env
+rf_stress_model.joblib

.github/workflows/docker-publish.yml

@@ -0,0 +1,43 @@
+name: Build and publish Docker image
+
+on:
+  push:
+    branches: [ main ]
+
+permissions:
+  contents: read
+  packages: write
+
+jobs:
+  build-and-push:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout repository
+        uses: actions/checkout@v4
+
+      - name: Set up QEMU
+        uses: docker/setup-qemu-action@v2
+
+      - name: Set up Docker Buildx
+        uses: docker/setup-buildx-action@v3
+
+      - name: Login to GitHub Container Registry
+        uses: docker/login-action@v2
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Compute lowercase owner
+        id: owner_lower
+        run: |
+          # GitHub repository_owner can contain uppercase letters; container registries require lowercase
+          owner=$(echo "${{ github.repository_owner }}" | tr '[:upper:]' '[:lower:]')
+          echo "owner=$owner" >> "$GITHUB_OUTPUT"
+
+      - name: Build and push image
+        uses: docker/build-push-action@v4
+        with:
+          push: true
+          tags: ghcr.io/${{ steps.owner_lower.outputs.owner }}/mindguard:latest
+          file: ./Dockerfile

.github/workflows/publish.yml

@@ -1,18 +1,39 @@
 name: Build and publish static demo site
 
 on:
+  # Run on pushes to any branch (except the generated gh-pages branch) so CI checks run before merge
   push:
+    # Run on all branches except the generated gh-pages branch
+    branches-ignore:
+      - 'gh-pages'
+    # Ignore generated artifacts to avoid workflow retrigger loops
+    paths-ignore:
+      - 'docs/**'
+      - 'rf_stress_model.joblib'
+  # Also run the workflow on pull requests targeting main so you can see results before merging
+  pull_request:
     branches: [ main ]
+    paths-ignore:
+      - 'docs/**'
+      - 'rf_stress_model.joblib'
 
 permissions:
   contents: write
+  pages: write
+  id-token: write
 
 jobs:
   build-and-publish:
     runs-on: ubuntu-latest
+    concurrency:
+      # Use a per-branch concurrency group so runs for different branches do not cancel each other
+      group: publish-docs-${{ github.ref_name }}
+      cancel-in-progress: true
     steps:
       - name: Checkout repository
         uses: actions/checkout@v4
+        with:
+          fetch-depth: 0
 
       - name: Set up Python
         uses: actions/setup-python@v4
@@ -29,10 +50,12 @@ jobs:
           python simple_demo.py
           python generate_site.py
 
-      - name: Commit generated docs
-        uses: EndBug/add-and-commit@v9
+      - name: Deploy site to gh-pages branch
+        uses: peaceiris/actions-gh-pages@v3
         with:
-          author_name: github-actions[bot]
-          author_email: github-actions[bot]@users.noreply.github.com
-          message: "ci: generate site (docs)"
-          add: 'docs'
+          github_token: ${{ secrets.GITHUB_TOKEN }}
+          publish_dir: ./docs
+          publish_branch: gh-pages
+          # keep repository history small by not committing other files
+          user_name: github-actions[bot]
+          user_email: github-actions[bot]@users.noreply.github.com

.gitignore

@@ -78,4 +78,7 @@ coverage/
 coverage.xml
 .cache/
 *.bak
-*.swp
+*.swp
+
+
+REPOSITORY_DETAILS.md

Dockerfile

@@ -0,0 +1,26 @@
+FROM python:3.10-slim
+
+# Prevent Python from writing .pyc files and buffering stdout/stderr
+ENV PYTHONDONTWRITEBYTECODE=1
+ENV PYTHONUNBUFFERED=1
+
+WORKDIR /app
+
+# Install system deps (minimal) and Python deps
+RUN apt-get update && apt-get install -y --no-install-recommends \
+    build-essential \
+    && rm -rf /var/lib/apt/lists/*
+
+COPY requirements.txt ./
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy project
+COPY . /app
+
+# Expose Streamlit default port
+EXPOSE 8501
+
+ENV STREAMLIT_SERVER_HEADLESS=true
+
+# Run the Streamlit app
+CMD ["streamlit", "run", "app.py", "--server.port=8501", "--server.address=0.0.0.0"]

README.md

@@ -61,6 +61,20 @@ If you don't have these components, use `simple_demo.py` to experiment quickly.
 Presentation and further info
 - The project presentation `MindGuard_An_Intelligent_Assistant_for_Student_Stress_Management_FinalProoposal.pptx` appears in the repository root. It likely contains project motivation, dataset descriptions, and proposed model architectures. If you want, I can extract its text and slides into a Markdown summary (I can do that automatically if you want me to install and run `python-pptx` locally).
 
+## Environment variables
+```
+GOOGLE_API_KEY=your_google_api_key_here
+```
+
+If `GOOGLE_API_KEY` is set, the Streamlit app (`app.py`) will call the Google Generative AI (Gemini) to create personalized recommendations. If the key is not set, the app now provides a safe, local fallback set of recommendations so the UI still works without external APIs.
+
+Note about Gemini quotas and errors
+- The Gemini API enforces quotas and rate limits. If your project exceeds the quota (or the API returns rate-limit errors), the application will now:
+   1. show a concise warning in the UI informing you that the external AI is unavailable, and
+   2. use a safe local fallback recommendation generator so users still receive helpful, non-medical advice.
+
+If you rely on Gemini for richer responses, monitor your Google Cloud quota and billing, or use a paid plan to increase rate limits. The app logs the full API error to the server console for debugging but intentionally avoids showing raw API errors in the UI.
+
  Files added/changed in this update
  - `README.md` — this file (expanded with aim, goals, and instructions).
  - `data/StressLevelDataset.csv` — small synthetic sample dataset (so the EDA and demo can run).
@@ -78,4 +92,25 @@ Presentation and further info
  Recommendations / result view:
 
  ![Recommendations view](assets/screenshots/recommendations_view.svg)
----
+
+Docker / container deployment
+--------------------------------
+I added a Dockerfile and docker-compose configuration so you can run the Streamlit app in a container or publish the image to a container registry.
+
+Run locally with Docker Compose
+
+1. Build and run:
+```bash
+docker compose up --build
+```
+2. Open the app at: http://localhost:8501
+
+Notes:
+- The container reads `GOOGLE_API_KEY` from the environment. You can provide it with `export GOOGLE_API_KEY=...` before running `docker compose up`, or create a `.env` file with that variable (do not commit secrets).
+- The image exposes port 8501.
+
+Publish image automatically (GitHub)
+
+- I added a GitHub Actions workflow `.github/workflows/docker-publish.yml`. On push to `main` it builds the image and pushes it to GitHub Container Registry (GHCR) as `ghcr.io/<owner>/mindguard:latest`.
+
+

app.py

@@ -4,6 +4,7 @@
 import os
 from dotenv import load_dotenv
 import google.generativeai as genai
+import joblib
 
 
 load_dotenv()
@@ -50,8 +51,8 @@ def get_h2o_model(model_path):
 def get_gemini_recommendations(stress_level_text, input_data):
     api_key = os.getenv("GOOGLE_API_KEY")
     if not api_key:
-        st.error("Google API Key not found. Please ensure it is set in the .env file.")
-        return None
+        # No Google API key — provide a safe local fallback recommendation instead
+        return generate_local_recommendations(stress_level_text, input_data)
     try:
         genai.configure(api_key=api_key)
         model = genai.GenerativeModel(model_name='gemini-2.5-pro')
@@ -90,18 +91,96 @@ def get_gemini_recommendations(stress_level_text, input_data):
         response = model.generate_content(prompt)
         return response.text
     except Exception as e:
-        st.error(f"An error occurred while calling Gemini AI: {e}")
-        return None
+        # Don't dump the raw API error to the user UI. Show a friendly message and fall back.
+        err_text = str(e)
+        # Detect common quota/rate-limit signals (simplified check)
+        if '429' in err_text or 'quota' in err_text.lower() or 'rate limit' in err_text.lower():
+            st.warning("The Gemini API is temporarily unavailable due to quota or rate limits. Showing local recommendations instead.")
+            st.info("To enable richer AI-generated advice, add a valid `GOOGLE_API_KEY` with an appropriate quota/billing plan. See the README for details.")
+        else:
+            st.warning("The Gemini AI service returned an error. Showing local recommendations instead.")
+
+        # Log the full error to the server console for debugging, but keep the UI message concise.
+        print("Gemini API error:", err_text)
+
+        # Fall back to local recommendations
+        return generate_local_recommendations(stress_level_text, input_data)
+
+
 
+def generate_local_recommendations(stress_level_text, input_data):
+    """Return a short, safe recommendation string when Gemini API is not available.
 
+    The content is intentionally generic and supportive (not medical advice).
+    """
+    # Extract a few key values safely
+    try:
+        sleep_q = input_data.get('sleep_quality', [None])[0]
+        depression = input_data.get('depression', [None])[0]
+        academic = input_data.get('academic_performance', [None])[0]
+        teacher_rel = input_data.get('teacher_student_relationship', [None])[0]
+    except Exception:
+        sleep_q = depression = academic = teacher_rel = None
+
+    support = (
+        "It sounds like you are going through a challenging time — that's understandable. "
+        "You're taking a good step by checking in on your wellbeing."
+    )
+
+    bullets = []
+    # Tailor bullets simply based on a few factors
+    if sleep_q is not None and sleep_q <= 2:
+        bullets.append("Try a 15-minute wind-down routine before bed to improve sleep quality.")
+    else:
+        bullets.append("Maintain regular sleep routines — consistent bed and wake times help a lot.")
 
-MODEL_PATH = "XGBoost_1_AutoML_1_20251102_85004"
-ml_model = get_h2o_model(MODEL_PATH)
+    if academic is not None and academic <= 2:
+        bullets.append("Break study tasks into 25-minute focused sessions with short breaks (Pomodoro).")
+    else:
+        bullets.append("Keep using structured study blocks and take short breaks to avoid burnout.")
+
+    if depression is not None and depression >= 15:
+        bullets.append("If low mood or worry persists, consider contacting campus mental health services or a trusted professional.")
+    elif teacher_rel is not None and teacher_rel <= 2:
+        bullets.append("Try reaching out to a trusted instructor or peer to discuss academic concerns.")
+    else:
+        bullets.append("Reach out to friends or family and share how you feel — social support helps.")
+
+    # Compose markdown-style text similar to Gemini output
+    md = f"**Support:** {support}\n\n**Recommendations:**\n"
+    for b in bullets:
+        md += f"- {b}\n"
+
+    return md
+
+
+
+# Prefer a lightweight scikit-learn model (created by `simple_demo.py`) when present.
+SKLEARN_MODEL_PATH = "rf_stress_model.joblib"
+use_sklearn = False
+sklearn_model = None
+if os.path.exists(SKLEARN_MODEL_PATH):
+    try:
+        sklearn_model = joblib.load(SKLEARN_MODEL_PATH)
+        use_sklearn = True
+        print(f"Loaded scikit-learn model from '{SKLEARN_MODEL_PATH}'. Streamlit will use this model (no Java/H2O required).")
+    except Exception as e:
+        print(f"Failed to load scikit-learn model '{SKLEARN_MODEL_PATH}': {e}. Falling back to H2O if available.")
+
+ml_model = None
+if not use_sklearn:
+    MODEL_PATH = "XGBoost_1_AutoML_1_20251102_85004"
+    ml_model = get_h2o_model(MODEL_PATH)
 
 st.title("🧠 MindGuard: Assess Your Stress Level")
 st.write("Answer a few questions to get an assessment of your current stress level and personalized recommendations.")
 
-if ml_model:
+# Proceed if either a scikit-learn model or the H2O model is available
+if use_sklearn or ml_model:
+    if use_sklearn:
+        st.info("Using local scikit-learn model (rf_stress_model.joblib) for predictions.")
+    else:
+        st.info("Using H2O model for predictions.")
     st.subheader("Please rate the following factors:")
     col1, col2 = st.columns(2)
     with col1:
@@ -130,11 +209,24 @@ def get_gemini_recommendations(stress_level_text, input_data):
         }
 
         input_df = pd.DataFrame(input_data)
-        h2o_input_frame = h2o.H2OFrame(input_df)
 
-        # 2. Make Prediction
-        prediction = ml_model.predict(h2o_input_frame)
-        predicted_level = prediction['predict'].as_data_frame().iloc[0, 0]
+        # 2. Make Prediction (scikit-learn fallback or H2O)
+        if use_sklearn and sklearn_model is not None:
+            try:
+                preds = sklearn_model.predict(input_df)
+                # sklearn returns integer labels (0/1/2)
+                predicted_level = float(preds[0])
+            except Exception as e:
+                st.error(f"Failed to predict with scikit-learn model: {e}")
+                predicted_level = None
+        else:
+            if ml_model is None:
+                st.error("No ML model available (H2O model failed to load and no scikit-learn fallback found).")
+                predicted_level = None
+            else:
+                h2o_input_frame = h2o.H2OFrame(input_df)
+                prediction = ml_model.predict(h2o_input_frame)
+                predicted_level = prediction['predict'].as_data_frame().iloc[0, 0]
         stress_map = {0.0: "Low", 1.0: "Medium", 2.0: "High"}
         predicted_stress_text = stress_map.get(predicted_level, "Unknown")
 

docker-compose.yml

@@ -0,0 +1,13 @@
+services:
+  mindguard:
+    build: .
+    image: mindguard:latest
+    ports:
+      - "8501:8501"
+    volumes:
+      # Mount current repo (read-only) so edits are visible during development
+      - ./:/app:ro
+    environment:
+      # Forward GOOGLE_API_KEY from host or .env
+      - GOOGLE_API_KEY=${GOOGLE_API_KEY}
+    restart: unless-stopped

docs/index.html

@@ -0,0 +1,32 @@
+
+<!doctype html>
+<html lang="en">
+  <head>
+    <meta charset="utf-8">
+    <meta name="viewport" content="width=device-width,initial-scale=1">
+    <title>MindGuard Demo</title>
+    <style>
+      body { font-family: Arial, Helvetica, sans-serif; background:#f8fafc; color:#0f172a; padding:24px }
+      .card { background: #fff; border-radius:8px; padding:18px; box-shadow: 0 4px 14px rgba(2,6,23,0.06); max-width:900px }
+      h1 { margin-top:0 }
+      .muted { color:#475569 }
+      .pill { background:#ecfeff; padding:8px 12px; border-radius:6px; display:inline-block; margin-top:8px }
+      pre { background:#0f172a; color:#f8fafc; padding:12px; border-radius:6px; overflow:auto }
+    </style>
+  </head>
+  <body>
+    <div class="card">
+      <h1>MindGuard — Live demo (static)</h1>
+      <p class="muted">This static page is auto-generated from the repository demo model. It shows a sample input, the predicted stress level, and simple recommendations.</p>
+
+      <h3>Sample input</h3>
+      <pre>{'sleep_quality': 3, 'teacher_student_relationship': 4, 'blood_pressure': 2, 'future_career_concerns': 3, 'depression': 8, 'academic_performance': 4, 'social_support': 2, 'self_esteem': 18, 'safety': 2, 'headache': 1, 'anxiety_level': 12, 'mental_health_history': 0, 'breathing_problem': 2, 'noise_level': 2, 'living_conditions': 2, 'basic_needs': 2, 'study_load': 2, 'peer_pressure': 2, 'extracurricular_activities': 1, 'bullying': 0}</pre>
+
+      <h3>Predicted stress level</h3>
+      <div class="pill">1 (Medium)</div>
+
+      <h3 style="margin-top:18px">Recommendation</h3>
+      <p>You're assessed at MEDIUM stress. Try 15-minute wind-down routines before bed, Pomodoro study blocks (25/5), and discuss career concerns with a mentor.</p>
+    </div>
+  </body>
+</html>