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import os
import requests
import streamlit as st
from transformers import pipeline
from PIL import Image
from dotenv import load_dotenv
# Load environment variables from .env file
load_dotenv()
# Set up the Hugging Face API URL and your API key
API_URL = "https://api-inference.huggingface.co/models/trpakov/vit-face-expression"
headers = {"Authorization": f"Bearer {os.getenv('HUGGINGFACE_API_KEY')}"}
# Function to query the Hugging Face model for facial expression
def query(filename):
with open(filename, "rb") as f:
data = f.read()
response = requests.post(API_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:
# Use GPT-Neo model for text generation
generator = pipeline('text-generation', model='EleutherAI/gpt-neo-125M')
prompt = f"Tell a joke that would cheer someone who is feeling {emotion}."
response = generator(prompt, max_length=50, num_return_sequences=1)
return response[0]['generated_text']
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
tts = gTTS(text, lang='en')
audio_file = "output.mp3"
tts.save(audio_file) # Save the audio file
return audio_file
# 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("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
st.audio(audio_file) # Streamlit will handle playback
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