update

app.py

@@ -1,55 +1,70 @@
-import os
-import requests
-import openai
-import streamlit as st
-from PIL import Image
-from dotenv import load_dotenv
-import torch
-from transformers import AutoProcessor, AutoModelForImageClassification
-
-# Load environment variables from .env file
+#############################################################################################################################
+# Filename   : app.py
+# Description: A Streamlit application to detect facial expressions from images and provide responses.
+# Author     : [Your Name]
+#
+# Copyright © 2024 by [Your Name]
+#############################################################################################################################
+
+# Import libraries.
+import os  # Load environment variable(s).
+import requests  # Send HTTP GET request to Hugging Face models for inference.
+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.
+import torch  # For tensor operations.
+from transformers import AutoProcessor, AutoModelForImageClassification  # Hugging Face models.
+import openai  # OpenAI API for generating text responses.
+
+#############################################################################################################################
+# Load environment variable(s).
 load_dotenv()
 
-# Set up OpenAI API key
+# Set up the Hugging Face API for emotion detection.
+HUGGINGFACE_API_TOKEN = os.getenv("HUGGINGFACE_API_TOKEN")
+# Set up OpenAI API key.
 openai.api_key = os.getenv('OPENAI_API_KEY')
 
-# Load the processor and model for facial expression recognition
+# Load the processor and model for facial expression recognition.
 processor = AutoProcessor.from_pretrained("trpakov/vit-face-expression")
 model = AutoModelForImageClassification.from_pretrained("trpakov/vit-face-expression")
 
-# Function to query the facial expression recognition model
+#############################################################################################################################
+# Function to query the facial expression recognition model.
 def query_emotion(image):
-    # Preprocess the image
+    # Preprocess the image.
     inputs = processor(images=image, return_tensors="pt")
 
-    # Perform inference
+    # Perform inference.
     with torch.no_grad():
         outputs = model(**inputs)
 
-    # Get predicted class index (the class with the highest logit)
+    # Get predicted class index (the class with the highest logit).
     logits = outputs.logits
     predicted_class_idx = torch.argmax(logits, dim=-1).item()
 
-    # Retrieve the label names from the model
-    label_names = model.config.id2label  # Mapping of indices to emotion labels
-    predicted_label = label_names[predicted_class_idx]  # Get the predicted label
+    # Retrieve the label names from the model.
+    label_names = model.config.id2label  # Mapping of indices to emotion labels.
+    predicted_label = label_names[predicted_class_idx]  # Get the predicted label.
 
     return predicted_label
 
-# Function to generate a response using OpenAI based on detected emotion
+#############################################################################################################################
+# 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.
         prompt = f"Generate a light-hearted joke or motivational message for someone who is feeling {emotion}."
-        
-        # Call OpenAI's API using GPT-4
+
+        # Call OpenAI's API using GPT-4.
         response = openai.ChatCompletion.create(
-            model="gpt-4",  # Specify the GPT-4 model
+            model="gpt-4",  # Specify the GPT-4 model.
             messages=[
                 {"role": "user", "content": prompt}
             ]
         )
 
-        # Extract the generated text
+        # Extract the generated text.
         generated_text = response['choices'][0]['message']['content']
         return generated_text.strip()
 
@@ -57,42 +72,50 @@ def generate_text_based_on_mood(emotion):
         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
+#############################################################################################################################
+# 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
+        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.")
+#############################################################################################################################
+# 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 uplifting messages or jokes.")
+
+    # Upload image.
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
 
-# 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)
 
-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)
+        st.write(f"Detected emotion: {emotion}")
 
-    # Detect facial expression
-    emotion = query_emotion(image)
-    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 remind you:")
+        st.write(message)
 
-    # 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)
 
-    # 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.
 
-    # 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()