import os
import requests
import streamlit as st
from transformers import pipeline
from PIL import Image
from dotenv import load_dotenv
import time

# Load environment variables from .env file
load_dotenv()

# Set up the Hugging Face API URLs and your API key
emotion_model_url = "https://api-inference.huggingface.co/models/trpakov/vit-face-expression"
text_model_url = "https://api-inference.huggingface.co/models/mrm8488/t5-base-finetuned-emotion"
headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}

# Function to query the facial expression recognition model
def query_emotion(filename):
    with open(filename, "rb") as f:
        data = f.read()
    response = requests.post(emotion_model_url, headers=headers, data=data)
    
    if response.status_code == 200:
        return response.json()
    else:
        st.error("Error detecting facial expression: " + response.text)
        return None

# Function to generate a joke or uplifting text based on the mood
def generate_text_based_on_mood(emotion):
    try:
        prompt = f"Generate a light-hearted joke or uplifting message for someone who is feeling {emotion}."
        
        # Retry mechanism for text generation
        for attempt in range(5):  # Retry up to 5 times
            response = requests.post(text_model_url, headers=headers, json={"inputs": prompt})
            if response.status_code == 200:
                generated_text = response.json()[0]['generated_text']
                if generated_text.strip():  # Ensure the response is not empty
                    return generated_text
                else:
                    st.warning("Received an empty joke, retrying...")
            elif response.status_code == 503:  # Service Unavailable
                st.warning("Model is loading, retrying...")
                time.sleep(5)  # Wait before retrying
            else:
                st.error("Error generating text: " + response.text)
                return "Sorry, I couldn't come up with a joke at this moment."
        
        st.error("Failed to generate text after multiple attempts.")
        return "Sorry, I couldn't come up with a joke at this moment."
        
    except Exception as e:
        st.error(f"Error generating text: {e}")
        return "Sorry, I couldn't come up with a joke at this moment."

# 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

# Streamlit UI
st.title("Facial Expression Mood Detector")
st.write("Upload an image of a face to detect mood and receive uplifting messages or jokes.")

# 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)

    # Save the uploaded file temporarily
    with open("uploaded_image.jpg", "wb") as f:
        f.write(uploaded_file.getbuffer())

    # Detect facial expression
    expression_output = query_emotion("uploaded_image.jpg")
    if expression_output:
        # Assuming the response has a 'label' field with the detected emotion
        emotion = expression_output[0]['label']  # Adjust based on response structure
        st.write(f"Detected emotion: {emotion}")

        # Generate text based on detected emotion
        joke = generate_text_based_on_mood(emotion)
        st.write("Here's something to cheer you up:")
        st.write(joke)

        # Convert the generated joke to audio
        audio_file = text_to_speech(joke)

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