app.py

# =========================================================
# AI DRIVER SAFETY DETECTION SYSTEM
# HuggingFace Gradio App
# =========================================================

import gradio as gr
import numpy as np
import cv2

from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.image import img_to_array

# =========================================================
# LOAD MODEL
# =========================================================

model = load_model("final_driver_state_model.h5")

# =========================================================
# CLASS LABELS
# IMPORTANT:
# Must match training class order exactly
# =========================================================

CLASS_NAMES = [
    "alert",
    "sleepy",
    "slowBlink",
    "yawning"
]

# =========================================================
# RISK LEVELS
# =========================================================

RISK_LEVELS = {
    "alert": "SAFE",
    "sleepy": "HIGH RISK",
    "slowBlink": "MEDIUM RISK",
    "yawning": "LOW RISK"
}

# =========================================================
# EMOJIS
# =========================================================

RISK_EMOJIS = {
    "SAFE": "🟢",
    "LOW RISK": "🟡",
    "MEDIUM RISK": "🟠",
    "HIGH RISK": "🔴"
}

# =========================================================
# IMAGE PREPROCESSING
# IMPORTANT:
# Match training preprocessing
# =========================================================

def preprocess_image(image):

    # -----------------------------------------------------
    # Gradio already provides RGB image
    # DO NOT use cvtColor
    # -----------------------------------------------------

    image = cv2.resize(image, (224, 224))

    image = image.astype("float32") / 255.0

    image = img_to_array(image)

    image = np.expand_dims(image, axis=0)

    return image

# =========================================================
# PREDICTION FUNCTION
# =========================================================

def predict_driver_state(image):

    if image is None:

        return (
            "Please upload an image.",
            {}
        )

    # =====================================================
    # PREPROCESS
    # =====================================================

    processed_image = preprocess_image(image)

    # =====================================================
    # PREDICTION
    # =====================================================

    prediction = model.predict(
        processed_image,
        verbose=0
    )

    # =====================================================
    # RESULTS
    # =====================================================

    class_index = int(np.argmax(prediction))

    predicted_class = CLASS_NAMES[class_index]

    confidence = float(np.max(prediction))

    risk_level = RISK_LEVELS[predicted_class]

    emoji = RISK_EMOJIS[risk_level]

    # =====================================================
    # CONFIDENCE SCORES
    # =====================================================

    confidence_scores = {}

    for i, class_name in enumerate(CLASS_NAMES):

        confidence_scores[class_name] = float(
            prediction[0][i]
        )

    # =====================================================
    # RESULT TEXT
    # =====================================================

    result = f"""
🚗 DRIVER STATE ANALYSIS

Prediction:
{predicted_class.upper()}

Confidence:
{confidence:.2f}

Risk Level:
{emoji} {risk_level}
"""

    return result, confidence_scores

# =========================================================
# TITLE & DESCRIPTION
# =========================================================

title = "🚗 AI Driver Safety Detection System"

description = """
Upload a driver image to analyze fatigue and attention state using Deep Learning.

## Supported Driver States
- 🟢 Alert
- 🔴 Sleepy
- 🟠 Slow Blink
- 🟡 Yawning

## AI Features
✅ CNN-Based Driver State Classification  
✅ Fatigue Risk Analysis  
✅ Deep Learning Inference  
✅ Real-Time Prediction Engine  
"""

# =========================================================
# GRADIO INTERFACE
# =========================================================

interface = gr.Interface(

    fn=predict_driver_state,

    inputs=gr.Image(
        type="numpy",
        label="Upload Driver Image"
    ),

    outputs=[

        gr.Textbox(
            label="Prediction Result"
        ),

        gr.Label(
            label="Confidence Scores"
        )
    ],

    title=title,

    description=description
)

# =========================================================
# LAUNCH
# =========================================================

interface.launch()