Spaces:
Running
Running
| ############################################################################################################################# | |
| # Filename : app.py | |
| # Description: A Streamlit application to detect facial expressions from images and provide responses. | |
| # Author : Lucas Yao | |
| # | |
| # Copyright © 2024 by Lucas Yao | |
| ############################################################################################################################# | |
| # 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. | |
| ############################################################################################################################# | |
| # Load environment variable(s). | |
| load_dotenv() | |
| # Set up OpenAI API key. | |
| openai.api_key = os.getenv('OPENAI_API_KEY') | |
| ############################################################################################################################# | |
| # 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 | |
| ############################################################################################################################# | |
| # 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." | |
| ############################################################################################################################# | |
| # 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 | |
| ############################################################################################################################# | |
| # 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() | |