import streamlit as st
import cv2
import numpy as np
import matplotlib.pyplot as plt
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model
from keras.models import load_model
import pandas as pd
from urllib import request
from io import BytesIO


# Load pre-trained emotion classification model
emotion_classification_model = load_model('./model/model_fine_tune.h5')  # Replace with actual path

# Load Haarcascades face detection classifier
face_cascade = cv2.CascadeClassifier('./model/haarcascade_frontalface_default.xml')

class_labels = ["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"]

def predict_emotion(frame):
    img = frame.to_ndarray(format="bgr24")

    gray_frame = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)

    # Detect faces
    faces = face_cascade.detectMultiScale(gray_frame, scaleFactor=1.1, minNeighbors=5, minSize=(30, 30))

    # Process each detected face
    for (x, y, w, h) in faces:
        face_img = img[y:y+h, x:x+w]

        # Preprocess the face image
        resized_face_img = cv2.resize(face_img, (48, 48))
        img_array = image.img_to_array(resized_face_img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array /= (255*117)  # Normalize the image

        # Perform emotion prediction using the loaded model
        inf_pred_single = emotion_classification_model.predict(img_array)

        max_pred_single = np.argsort(inf_pred_single[0])[-2:][::-1]

        data_inf_single = []
        rank = []

        for i in inf_pred_single[0]:
            value = i * 100
            rank.append(value)
            data_inf_single.append(f'{value.round(2)}%')

        rank = (-np.array(rank)).argsort()[:2]

        pred_class_single = pd.DataFrame(class_labels).loc[rank][0].tolist()

        # Draw bounding box and emotion label on the frame
        cv2.rectangle(img, (x, y), (x + w, y + h), (255, 0, 0), 2)
        cv2.putText(img, f'Emotion: {pred_class_single[0]}', (x, y - 10), cv2.FONT_HERSHEY_SIMPLEX, 0.9, (255, 0, 0), 2)

    return img  # Return the annotated frame

def image_prediction():
    
       
    emotion_classification_model = load_model('./model/model_fine_tune.h5')
    
    
    url = "https://lh3.googleusercontent.com/0e8O0JPOp_ydq7wqv6kgYz6UpF9w_INfnOLIhnJZBEHFcWIygkuLa3SVghhGYgE0XWzQYBPb6wb1eQFN0pVIAYlzEeNojYuCWg=s0"
    
    def img_url(url):
        res = request.urlopen(url).read()
        img_ori = image.load_img(BytesIO(res))
        img = image.load_img(BytesIO(res), target_size=(48, 48), keep_aspect_ratio=True)
        show_predict(img, img_ori)
        
    def show_predict(img, img_ori):
        col1, col2 = st.columns(2)
        fig = plt.figure()
        plt.imshow(img_ori)
        plt.axis('off')
        col1.pyplot(fig)
        
        img_array = image.img_to_array(img)
        img_array = np.expand_dims(img_array, axis=0)
        img_array /= (255*117)  # Normalize the image

        inf_pred_single = emotion_classification_model.predict(img_array)
        
        data_inf_single = []

        rank = []

        for i in inf_pred_single[0]:
            value = i * 100
            rank.append(value)
            data_inf_single.append(f'{value.round(2)}%')

        rank = (-np.array(rank)).argsort()[:2]
        
        pred_class_single = pd.DataFrame(class_labels).loc[rank][0].tolist()

        prediction_result_single = pd.DataFrame(columns=["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"])
        prediction_result_single.loc[len(prediction_result_single)] = data_inf_single

        prediction_result_single
        
        st.markdown("""
                    <style>
                    .big-font {
                        font-size:30px !important;
                    }
                    </style>
                    """, unsafe_allow_html=True)
        
        col2.write('Prediction Class:')
        col2.markdown(f'<p class="big-font">{pred_class_single[0].capitalize()}</p>', unsafe_allow_html=True)
        
        col2.dataframe(prediction_result_single.set_index(prediction_result_single.columns[0]), use_container_width=True)
    
    class_labels = ["angry", "disgusted", "fearful", "happy", "neutral", "sad", "surprised"]

    st.write('Insert Image URL Below (Make sure face is centered and fitted)')
    
    st.markdown('[Example Image](https://cdn.idntimes.com/content-images/community/2021/12/whatsapp-image-2021-12-02-at-190446-8ecf63e1fa6b5c8c5e9ac43034bc86d3-c563813ea99f16a795ad4c53af10881a_600x400.jpeg)')
    
    col1, col2 = st.columns((9,1))
    url_input = col1.text_input(label="Image Links")
    
    st.markdown(
                """
            <style>
            button {
                height: auto;
                margin-top: 28px !important;
                padding-left: 24px !important;
                padding-right: 24px !important;
            }
            </style>
            """,
                unsafe_allow_html=True,
            )
    pred_button = col2.button(label="Predict")
    
    if pred_button:
        img_url(url_input)
    else:
        img_url(url)