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README.md

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+# ✈️ Travel Demand Forecaster — Prophet + Streamlit
+
+A time series forecasting app for travel demand using Facebook Prophet, deployed via CI/CD to Hugging Face Spaces.
+
+---
+
+## 🚀 CI/CD Pipeline
+
+```
+Push code to GitHub
+        ↓
+GitHub Actions triggers
+        ↓
+🧪 Run all tests (CI)
+        ↓
+✅ Tests pass?
+        ↓
+🤗 Auto deploy to Hugging Face (CD)
+        ↓
+🌐 App is live!
+```
+
+---
+
+## 🛠️ Setup Instructions
+
+### Step 1 — Fork or clone this repo
+```bash
+git clone https://github.com/YOUR_USERNAME/travel-prophet.git
+cd travel-prophet
+```
+
+### Step 2 — Create Hugging Face Space
+1. Go to https://huggingface.co/spaces
+2. Click **Create new Space**
+3. Choose **Streamlit** as SDK
+4. Name it e.g. `travel-prophet-forecaster`
+
+### Step 3 — Add HF Token to GitHub Secrets
+1. Go to https://huggingface.co/settings/tokens
+2. Create a new token with **write** access
+3. Go to your GitHub repo → **Settings** → **Secrets and variables** → **Actions**
+4. Click **New repository secret**
+5. Name: `HF_TOKEN`, Value: (paste your token)
+
+### Step 4 — Update deploy.yml
+In `.github/workflows/deploy.yml`, replace:
+```
+YOUR_HF_USERNAME  →  your Hugging Face username
+YOUR_SPACE_NAME   →  your Space name
+```
+
+### Step 5 — Push to GitHub
+```bash
+git add .
+git commit -m "Initial commit"
+git push origin main
+```
+
+### Step 6 — Watch it deploy! 🎉
+Go to your GitHub repo → **Actions** tab to watch the pipeline run.
+
+---
+
+## 📁 Project Structure
+
+```
+travel-prophet/
+├── app.py                          # Main Streamlit app
+├── requirements.txt                # Dependencies
+├── tests/
+│   └── test_app.py                 # All tests (CI runs these)
+├── .github/
+│   └── workflows/
+│       └── deploy.yml              # CI/CD pipeline definition
+└── README.md
+```
+
+---
+
+## 🧪 Run Tests Locally
+
+```bash
+pip install -r requirements.txt
+pip install pytest
+pytest tests/ -v
+```
+
+---
+
+## 📊 Features
+
+- Upload your own CSV travel data
+- Sample data included for demo
+- Adjustable forecast period (30–365 days)
+- Yearly and weekly seasonality
+- Confidence intervals
+- Downloadable forecast CSV
+- Interactive Plotly charts
+
+---
+
+## 🔄 How CI/CD Works
+
+| Event | What Happens |
+|-------|-------------|
+| Push to `main` | Tests run → if pass → deploy to HF |
+| Pull Request | Tests run only → no deployment |
+| Tests fail | Pipeline stops → no deployment |
+| Tests pass | Auto deploys to Hugging Face Spaces |

app.py

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+import streamlit as st
+import pandas as pd
+import numpy as np
+from prophet import Prophet
+import plotly.graph_objects as go
+from datetime import datetime, timedelta
+
+# ======================================
+# Page Config
+# ======================================
+st.set_page_config(
+    page_title="✈️ Travel Demand Forecaster",
+    page_icon="✈️",
+    layout="wide"
+)
+
+st.title("✈️ Travel Demand Forecaster")
+st.markdown("**Powered by Facebook Prophet** — Upload your travel data or use sample data to forecast future demand.")
+
+# ======================================
+# Sidebar Controls
+# ======================================
+st.sidebar.header("⚙️ Forecast Settings")
+
+forecast_days = st.sidebar.slider(
+    "Forecast Period (days)", 
+    min_value=30, 
+    max_value=365, 
+    value=90,
+    step=30
+)
+
+seasonality_mode = st.sidebar.selectbox(
+    "Seasonality Mode",
+    ["multiplicative", "additive"],
+    index=0
+)
+
+yearly_seasonality = st.sidebar.checkbox("Yearly Seasonality", value=True)
+weekly_seasonality = st.sidebar.checkbox("Weekly Seasonality", value=True)
+daily_seasonality = st.sidebar.checkbox("Daily Seasonality", value=False)
+
+# ======================================
+# Data Input
+# ======================================
+st.subheader("📂 Data Input")
+
+data_option = st.radio(
+    "Choose data source:",
+    ["Use Sample Travel Data", "Upload CSV File"],
+    horizontal=True
+)
+
+def generate_sample_data():
+    """Generate realistic travel demand sample data"""
+    np.random.seed(42)
+    dates = pd.date_range(start="2021-01-01", end="2023-12-31", freq="D")
+    
+    # Base demand with trend
+    trend = np.linspace(100, 150, len(dates))
+    
+    # Yearly seasonality (peak in summer and holidays)
+    yearly = 30 * np.sin(2 * np.pi * np.arange(len(dates)) / 365 - np.pi/2)
+    
+    # Weekly seasonality (weekends higher)
+    weekly = 10 * np.sin(2 * np.pi * np.arange(len(dates)) / 7)
+    
+    # Random noise
+    noise = np.random.normal(0, 8, len(dates))
+    
+    demand = trend + yearly + weekly + noise
+    demand = np.maximum(demand, 10)  # no negative demand
+    
+    df = pd.DataFrame({"ds": dates, "y": demand.round(0)})
+    return df
+
+if data_option == "Use Sample Travel Data":
+    df = generate_sample_data()
+    st.success("✅ Sample travel demand data loaded! (Jan 2021 — Dec 2023)")
+    st.dataframe(df.tail(10), use_container_width=True)
+
+else:
+    uploaded_file = st.file_uploader(
+        "Upload CSV with 'ds' (date) and 'y' (value) columns",
+        type=["csv"]
+    )
+    
+    if uploaded_file:
+        df = pd.read_csv(uploaded_file)
+        df['ds'] = pd.to_datetime(df['ds'])
+        st.success(f"✅ Loaded {len(df)} rows of data!")
+        st.dataframe(df.tail(10), use_container_width=True)
+    else:
+        st.info("👆 Please upload a CSV file with columns: **ds** (date) and **y** (value)")
+        st.stop()
+
+# ======================================
+# Train & Forecast
+# ======================================
+st.divider()
+st.subheader("🔮 Forecast")
+
+if st.button("🚀 Generate Forecast", type="primary"):
+    with st.spinner("Training Prophet model..."):
+        
+        # Train model
+        model = Prophet(
+            seasonality_mode=seasonality_mode,
+            yearly_seasonality=yearly_seasonality,
+            weekly_seasonality=weekly_seasonality,
+            daily_seasonality=daily_seasonality
+        )
+        model.fit(df)
+        
+        # Make future dataframe
+        future = model.make_future_dataframe(periods=forecast_days)
+        forecast = model.predict(future)
+        
+        st.success(f"✅ Forecast generated for next {forecast_days} days!")
+
+    # ======================================
+    # Plot Results
+    # ======================================
+    col1, col2 = st.columns(2)
+
+    with col1:
+        st.metric(
+            "Average Forecasted Demand",
+            f"{forecast['yhat'].tail(forecast_days).mean():.0f}",
+            delta=f"+{forecast['yhat'].tail(forecast_days).mean() - df['y'].mean():.0f} vs historical"
+        )
+
+    with col2:
+        st.metric(
+            "Peak Forecasted Demand",
+            f"{forecast['yhat'].tail(forecast_days).max():.0f}",
+            delta="Next period peak"
+        )
+
+    # Main forecast plot
+    fig = go.Figure()
+
+    # Historical data
+    fig.add_trace(go.Scatter(
+        x=df['ds'], y=df['y'],
+        name='Historical',
+        mode='lines',
+        line=dict(color='#1f77b4', width=1.5)
+    ))
+
+    # Forecast
+    forecast_future = forecast[forecast['ds'] > df['ds'].max()]
+    fig.add_trace(go.Scatter(
+        x=forecast_future['ds'], y=forecast_future['yhat'],
+        name='Forecast',
+        mode='lines',
+        line=dict(color='#ff7f0e', width=2, dash='dash')
+    ))
+
+    # Confidence interval
+    fig.add_trace(go.Scatter(
+        x=pd.concat([forecast_future['ds'], forecast_future['ds'][::-1]]),
+        y=pd.concat([forecast_future['yhat_upper'], forecast_future['yhat_lower'][::-1]]),
+        fill='toself',
+        fillcolor='rgba(255,127,14,0.2)',
+        line=dict(color='rgba(255,255,255,0)'),
+        name='Confidence Interval'
+    ))
+
+    fig.update_layout(
+        title="Travel Demand Forecast",
+        xaxis_title="Date",
+        yaxis_title="Demand",
+        hovermode='x unified',
+        height=450
+    )
+
+    st.plotly_chart(fig, use_container_width=True)
+
+    # Components plot
+    st.subheader("📊 Forecast Components")
+    
+    col1, col2 = st.columns(2)
+
+    with col1:
+        # Trend
+        fig_trend = go.Figure()
+        fig_trend.add_trace(go.Scatter(
+            x=forecast['ds'], y=forecast['trend'],
+            mode='lines', line=dict(color='green', width=2)
+        ))
+        fig_trend.update_layout(title="Trend", height=300)
+        st.plotly_chart(fig_trend, use_container_width=True)
+
+    with col2:
+        # Yearly seasonality
+        if yearly_seasonality:
+            fig_yearly = go.Figure()
+            fig_yearly.add_trace(go.Scatter(
+                x=forecast['ds'], y=forecast['yearly'],
+                mode='lines', line=dict(color='purple', width=2)
+            ))
+            fig_yearly.update_layout(title="Yearly Seasonality", height=300)
+            st.plotly_chart(fig_yearly, use_container_width=True)
+
+    # Forecast table
+    st.subheader("📋 Forecast Data")
+    forecast_display = forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(forecast_days)
+    forecast_display.columns = ['Date', 'Forecast', 'Lower Bound', 'Upper Bound']
+    forecast_display = forecast_display.round(2)
+    st.dataframe(forecast_display, use_container_width=True)
+
+    # Download button
+    csv = forecast_display.to_csv(index=False)
+    st.download_button(
+        label="📥 Download Forecast CSV",
+        data=csv,
+        file_name="travel_forecast.csv",
+        mime="text/csv"
+    )

requirements.txt

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+streamlit==1.32.0
+prophet==1.1.5
+pandas==2.0.3
+numpy==1.24.3
+plotly==5.18.0
+pystan==3.7.0