#############################################################################################################################
# Filename   : app.py
# Description: A Streamlit application to detect facial expressions from images and provide responses.
# Author     : Lucas Yao
#
# Copyright © 2024 by Lucas Yao
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# Import libraries.
import os  # Load environment variable(s).
import streamlit as st  # Build the GUI of the application.
from PIL import Image  # Handle image operations.
from dotenv import load_dotenv  # Load environment variables.
from fer import FER  # Import the FER model for facial expression recognition.
import openai  # OpenAI API for generating text responses.

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# Load environment variable(s).
load_dotenv()

# Set up OpenAI API key.
openai.api_key = os.getenv('OPENAI_API_KEY')

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# Function to query the facial expression recognition model using FER.
def query_emotion(image):
    detector = FER()
    emotions = detector.detect_emotions(image)
    
    if emotions:
        # Get the emotion with the highest score.
        dominant_emotion = max(emotions[0]['emotions'], key=emotions[0]['emotions'].get)
        return dominant_emotion
    else:
        st.error("Could not detect any emotion.")
        return None

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# Function to generate a response using OpenAI based on detected emotion.
def generate_text_based_on_mood(emotion, response_type):
    try:
        if response_type == "Joke":
            prompt = f"Generate a light-hearted joke for someone who is feeling {emotion}."
        else:  # Motivational Message
            prompt = f"Generate a motivational message for someone who is feeling {emotion}."
        
        # Call OpenAI's API using GPT-4.
        response = openai.ChatCompletion.create(
            model="gpt-4",  # Specify the GPT-4 model
            messages=[
                {"role": "user", "content": prompt}
            ]
        )

        # Extract the generated text.
        generated_text = response['choices'][0]['message']['content']
        return generated_text.strip()

    except Exception as e:
        st.error(f"Error generating text: {e}")
        return "Sorry, I couldn't come up with a message at this moment."

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# Function to convert text to speech using gTTS.
def text_to_speech(text):
    from gtts import gTTS
    try:
        tts = gTTS(text, lang='en')
        audio_file = "output.mp3"
        tts.save(audio_file)  # Save the audio file.
        return audio_file
    except Exception as e:
        st.error(f"Error with TTS: {e}")
        return None

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# Main function to create the Streamlit web application.
def main():
    st.title("Facial Expression Mood Detector")
    st.write("Upload an image of a face to detect mood and receive a response.")

    # Upload image.
    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])

    if uploaded_file is not None:
        # Load and display the image.
        image = Image.open(uploaded_file)
        st.image(image, caption='Uploaded Image', use_column_width=True)

        # Detect facial expression.
        emotion = query_emotion(image)
        if emotion:
            st.write(f"Detected emotion: {emotion}")

            # Dropdown for selecting response type.
            response_type = st.selectbox("Select the type of response:", ["Joke", "Motivational Message"])

            # Generate text based on detected emotion and user preference.
            if st.button("Get Response"):
                message = generate_text_based_on_mood(emotion, response_type)
                st.write("Here's your response:")
                st.write(message)

                # Convert the generated message to audio.
                audio_file = text_to_speech(message)

                # Provide an audio player in the Streamlit app if audio file exists.
                if audio_file:
                    st.audio(audio_file)  # Streamlit will handle playback.

#############################################################################################################################
# Run the application.
if __name__ == "__main__":
    main()