app.py

# AI Assistance Declaration:
# Parts of this script were assisted by ChatGPT GPT.
# AI assistance was limited to code suggestions, debugging tips, or documentation wording.
# All algorithmic design, implementation decisions, and final code structure were created and verified by the project team.

import gradio as gr
import requests

# Cloud Run API URL
API_BASE = "https://ecommerce-ml-346297770564.us-east1.run.app"

# Numeric features
numeric_features = {
    "shipping_fee": 6,
    "tax": 6,
    "avg_order_value": 6,
    "day_of_week": 6
}

# Binary features (0/1) but UI uses "Yes"/"No"
binary_features = {
    "status_cancelled": 1,
    "status_completed": 1,
    "status_processing": 1,
    "status_returned": 0,
    "status_shipped": 1,
    "is_holiday": 1
}

# Create widgets
numeric_inputs = [
    gr.Number(label=f"📊 {f.replace('_',' ').title()}", value=v)
    for f, v in numeric_features.items()
]

binary_inputs = [
    gr.Radio(
        label=f"🔘 {f.replace('_',' ').title()}",
        choices=["Yes (1)", "No (0)"],
        value="Yes (1)" if v == 1 else "No (0)"
    )
    for f, v in binary_features.items()
]

feature_names = list(numeric_features.keys()) + list(binary_features.keys())
all_inputs = numeric_inputs + binary_inputs


def parse_value(v):
    if isinstance(v, str):
        return 1 if "Yes" in v else 0
    return v


def predict_fn(*values):
    parsed_vals = [parse_value(v) for v in values]
    features = {f: v for f, v in zip(feature_names, parsed_vals)}

    payload = {"features": features}
    response = requests.post(f"{API_BASE}/predict", json=payload)

    if response.status_code != 200:
        return f"❌ API Error: {response.text}"

    preds = response.json()["predictions"]

    # prediction UI
    html = """
    <div style='
        background: #f9fafb;
        border-left: 6px solid #4f46e5;
        padding: 15px 20px;
        border-radius: 10px;
        font-size: 16px;
    '>
        <h3 style='margin-top: 0; color: #4f46e5;'>📦 Prediction Results</h3>
    """
    for k, v in preds.items():
        html += f"""
        <p><b>{k}:</b> {v:.4f}</p>
        """
    html += "</div>"

    return html


def reset_inputs():
    merged = list(numeric_features.values()) + list(binary_features.values())
    return [
        v if isinstance(v, (int, float)) else ("Yes (1)" if v == 1 else "No (0)")
        for v in merged
    ]


# UI with Blocks
with gr.Blocks() as demo:

    gr.HTML("""
        <style>
            .gradio-container {
                max-width: 900px !important;
                margin: auto !important;
            }
        </style>
    """)

    gr.Markdown("## 🚀 **E-commerce Sales Forecast Demo**")
    gr.Markdown(
        "Enter feature values below. Boolean features use **Yes/No** but backend receives **1/0**.\n"
        "Click **Predict** to get results from the model deployed on **Google Cloud Run**."
    )

    with gr.Row():
        with gr.Column():
            gr.Markdown("### 📊 Numeric Features")
            for widget in numeric_inputs:
                widget.render()

            gr.Markdown("---")
            gr.Markdown("### 🔘 Binary Features (Yes / No)")
            for widget in binary_inputs:
                widget.render()

        with gr.Column():
            result_box = gr.HTML(label="📦 Predictions")

    with gr.Row():
        predict_btn = gr.Button("🔮 Predict", variant="primary")
        reset_btn = gr.Button("♻️ Reset")

    predict_btn.click(fn=predict_fn, inputs=all_inputs, outputs=result_box)
    reset_btn.click(fn=lambda: reset_inputs(), inputs=None, outputs=all_inputs)

    gr.Markdown("This interface communicates with a FastAPI backend deployed on **Cloud Run**.")

demo.launch()