import os
import streamlit as st
from huggingface_hub import HfApi
from PIL import Image
import sqlite3
import cv2
import numpy as np
from tensorflow.keras.models import load_model  # Importing load_model
from datetime import datetime  # Importing datetime

# Constants
HOME_DIR = os.getcwd()  # Home directory (root directory)
DATABASE = "students.db"  # SQLite database to store student information
REPO_NAME = "face-and-emotion-detection"
REPO_ID = f"LovnishVerma/{REPO_NAME}"  # Hugging Face Repo
EMOTION_MODEL_FILE = "CNN_Model_acc_75.h5"  # Emotion detection model file
EMOTION_LABELS = ["Angry", "Disgust", "Fear", "Happy", "Sad", "Surprise", "Neutral"]

# Retrieve Hugging Face token from environment variable
hf_token = os.getenv("upload")
if not hf_token:
    st.error("Hugging Face token not found. Please set the environment variable.")
    st.stop()

# Initialize Hugging Face API
api = HfApi()
try:
    api.create_repo(repo_id=REPO_ID, repo_type="space", space_sdk="streamlit", token=hf_token, exist_ok=True)
    st.success(f"Repository '{REPO_NAME}' is ready on Hugging Face!")
except Exception as e:
    st.error(f"Error creating Hugging Face repository: {e}")

# Load the emotion detection model
try:
    # Check if model file exists
    if not os.path.exists(EMOTION_MODEL_FILE):
        st.error(f"Error: Emotion model file '{EMOTION_MODEL_FILE}' not found!")
        st.stop()

    # Load the model
    emotion_model = load_model(EMOTION_MODEL_FILE)  # Load the emotion model
    st.success("Emotion detection model loaded successfully!")
except Exception as e:
    st.error(f"Error loading emotion model: {e}")
    st.stop()

# Database Functions
def initialize_database():
    """ Initializes the SQLite database by creating the students table if it doesn't exist. """
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()
    cursor.execute("""
        CREATE TABLE IF NOT EXISTS students (
            id INTEGER PRIMARY KEY AUTOINCREMENT,
            name TEXT NOT NULL,
            roll_no TEXT NOT NULL UNIQUE,
            image_url TEXT NOT NULL,
            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP
        )
    """)
    conn.commit()
    conn.close()

def save_to_database(name, roll_no, image_url):
    """ Saves the student's data to the database. """
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()
    try:
        cursor.execute("""
            INSERT INTO students (name, roll_no, image_url)
            VALUES (?, ?, ?)
        """, (name, roll_no, image_url))
        conn.commit()
        st.success("Data saved successfully!")
    except sqlite3.IntegrityError:
        st.error("Roll number already exists!")
    finally:
        conn.close()

def save_image_to_hugging_face(image, name, roll_no):
    """ Saves the image locally to the HOME_DIR and uploads it to Hugging Face. """
    # Construct the local file path
    filename = f"{name}_{roll_no}_{datetime.now().strftime('%Y%m%d%H%M%S')}.jpg"
    local_path = os.path.join(HOME_DIR, filename)
    
    try:
        # Convert image to RGB if necessary
        if image.mode != "RGB":
            image = image.convert("RGB")
        
        # Save the image to the home directory
        image.save(local_path)
        
        # Upload the saved file to Hugging Face
        api.upload_file(
            path_or_fileobj=local_path,
            path_in_repo=filename,
            repo_id=REPO_ID,
            repo_type="space",
            token=hf_token,
        )

        # Construct the image URL for Hugging Face
        image_url = f"https://{REPO_NAME}.hf.space/media/{filename}"
        st.success(f"Image saved to Hugging Face as {filename}. URL: {image_url}")

    except Exception as e:
        st.error(f"Error saving or uploading image: {e}")
    
    return image_url

# Initialize the database when the app starts
initialize_database()

# Streamlit user interface (UI)
st.title("Student Registration with Hugging Face Image Upload")

# Input fields for student details
name = st.text_input("Enter your name")
roll_no = st.text_input("Enter your roll number")

# Choose input method for the image (webcam or file upload)
capture_mode = st.radio("Choose an option to upload your image", ["Use Webcam", "Upload File"])

# Handle webcam capture or file upload
if capture_mode == "Use Webcam":
    picture = st.camera_input("Take a picture")  # Capture image using webcam
elif capture_mode == "Upload File":
    picture = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])  # Upload image from file system

# Save data and process image on button click
if st.button("Register"):
    if not name or not roll_no:
        st.error("Please fill in both name and roll number.")
    elif not picture:
        st.error("Please upload or capture an image.")
    else:
        try:
            # Open the image based on capture mode
            if capture_mode == "Use Webcam" and picture:
                image = Image.open(picture)
            elif capture_mode == "Upload File" and picture:
                image = Image.open(picture)
            
            # Save the image locally and upload it to Hugging Face
            image_url = save_image_to_hugging_face(image, name, roll_no)
            save_to_database(name, roll_no, image_url)
        except Exception as e:
            st.error(f"An error occurred: {e}")

# Display registered student data
if st.checkbox("Show registered students"):
    conn = sqlite3.connect(DATABASE)
    cursor = conn.cursor()
    cursor.execute("SELECT name, roll_no, image_url, timestamp FROM students")
    rows = cursor.fetchall()
    conn.close()
    
    st.write("### Registered Students")
    for row in rows:
        name, roll_no, image_url, timestamp = row
        st.write(f"**Name:** {name}, **Roll No:** {roll_no}, **Timestamp:** {timestamp}")
        st.image(image_url, caption=f"{name} ({roll_no})", use_column_width=True)

# Face and Emotion Detection Function
def detect_faces_and_emotions(image):
    gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
    face_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')
    faces = face_cascade.detectMultiScale(gray_image, scaleFactor=1.3, minNeighbors=5)
    
    for (x, y, w, h) in faces:
        face = gray_image[y:y+h, x:x+w]
        resized_face = cv2.resize(face, (48, 48))  # Resize face to 48x48
        rgb_face = cv2.cvtColor(resized_face, cv2.COLOR_GRAY2RGB)
        normalized_face = rgb_face / 255.0
        reshaped_face = np.reshape(normalized_face, (1, 48, 48, 3))
        
        # Predict the emotion
        emotion_prediction = emotion_model.predict(reshaped_face)
        emotion_label = np.argmax(emotion_prediction)
        return EMOTION_LABELS[emotion_label]
    return None

# UI for Emotion Detection
if st.sidebar.selectbox("Menu", ["Register Student", "Face Recognition and Emotion Detection", "View Attendance"]) == "Face Recognition and Emotion Detection":
    st.subheader("Recognize Faces and Detect Emotions")
    action = st.radio("Choose Action", ["Upload Image", "Use Webcam"])

    if action == "Upload Image":
        uploaded_file = st.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
        if uploaded_file:
            img = Image.open(uploaded_file)
            img_array = np.array(img)
            emotion_label = detect_faces_and_emotions(img_array)
            if emotion_label:
                st.success(f"Emotion Detected: {emotion_label}")
            else:
                st.warning("No face detected.")

    elif action == "Use Webcam":
        st.info("Use the camera input widget to capture an image.")
        camera_image = st.camera_input("Take a picture")
        if camera_image:
            img = Image.open(camera_image)
            img_array = np.array(img)
            emotion_label = detect_faces_and_emotions(img_array)
            if emotion_label:
                st.success(f"Emotion Detected: {emotion_label}")
            else:
                st.warning("No face detected.")