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| import os | |
| import requests | |
| import openai | |
| import streamlit as st | |
| 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 URL and your API key | |
| emotion_model_url = "https://api-inference.huggingface.co/models/trpakov/vit-face-expression" | |
| headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"} | |
| # Set up OpenAI API key | |
| openai.api_key = os.getenv('OPENAI_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 response using OpenAI based on detected emotion | |
| def generate_text_based_on_mood(emotion): | |
| try: | |
| # Create a dynamic prompt based on the detected emotion | |
| if emotion == "happy": | |
| prompt = "Give a motivational quote to celebrate happiness." | |
| elif emotion == "sad": | |
| prompt = "Provide a comforting message for someone feeling sad." | |
| elif emotion == "angry": | |
| prompt = "Suggest a way to calm down someone feeling angry." | |
| elif emotion == "fear": | |
| prompt = "Give an encouraging message for someone feeling fearful." | |
| elif emotion == "surprised": | |
| prompt = "Offer a fun fact or light-hearted comment for someone feeling surprised." | |
| elif emotion == "neutral": | |
| prompt = "Provide a general motivational quote." | |
| # Call OpenAI's API using the new interface | |
| 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 | |
| # 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 | |
| message = generate_text_based_on_mood(emotion) | |
| st.write("Here's something to cheer you up:") | |
| 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 | |