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import streamlit as st
from transformers import pipeline
# Emotion classifier (use a pre-trained model from Hugging Face)
emotion_analyzer = pipeline("text-classification", model="distilbert-base-uncased")
# Enhanced Suggestion Database with resources
suggestion_database = {
"NEGATIVE": {
"suggestions": ["Try a guided meditation", "Take a walk in nature", "Connect with a friend"],
"articles": [
{"title": "Overcoming Sadness", "url": "https://example.com/sadness1"},
{"title": "Understanding Depression", "url": "https://example.com/sadness2"},
],
"videos": [
{"title": "Mindfulness for Sadness", "url": "https://www.youtube.com/watch?v=sadnessvideo1"},
{"title": "Coping with Grief", "url": "https://www.youtube.com/watch?v=sadnessvideo2"},
],
},
"POSITIVE": {
"suggestions": ["Practice gratitude", "Engage in a hobby", "Spend time with loved ones"],
"articles": [
{"title": "The Benefits of Joy", "url": "https://example.com/joy1"},
{"title": "Maintaining Positive Emotions", "url": "https://example.com/joy2"},
],
"videos": [
{"title": "Boosting Your Happiness", "url": "https://www.youtube.com/watch?v=joyvideo1"},
{"title": "Practicing Gratitude", "url": "https://www.youtube.com/watch?v=joyvideo2"},
],
},
"NEUTRAL": {
"suggestions": ["Take a break", "Engage in a relaxing activity", "Spend time in nature"],
"articles": [
{"title": "Importance of Self-Care", "url": "https://example.com/selfcare1"},
{"title": "Stress Management Techniques", "url": "https://example.com/stress1"},
],
"videos": [
{"title": "Relaxation Techniques", "url": "https://www.youtube.com/watch?v=relaxvideo1"},
{"title": "Mindfulness Exercises", "url": "https://www.youtube.com/watch?v=mindfulnessvideo1"},
],
}
}
# Function to fetch relevant resources based on emotion
def get_relevant_resources(emotion):
resources = suggestion_database.get(emotion, {})
return resources.get("suggestions", []), resources.get("articles", []), resources.get("videos", [])
# Function to suggest activities based on the emotion analysis result
def suggest_activity(emotion_analysis):
max_emotion = max(emotion_analysis, key=emotion_analysis.get) if emotion_analysis else "NEUTRAL"
suggestions, articles, videos = get_relevant_resources(max_emotion)
return {
"suggestions": suggestions,
"articles": articles,
"videos": videos,
}
# Streamlit app
def main():
st.title("Emotion Detection and Suggestions")
st.write("Please answer the following questions:")
# Step 1: Collect answers to three questions
question_1 = st.text_input("How are you feeling today? (e.g., happy, sad, stressed)")
question_2 = st.text_input("What's something that is currently on your mind?")
question_3 = st.text_input("Do you feel overwhelmed or calm right now?")
# Step 2: Analyze sentiment of responses (only proceed if all questions are answered)
if question_1 and question_2 and question_3:
text_to_analyze = f"{question_1} {question_2} {question_3}"
analysis_result = emotion_analyzer(text_to_analyze)
emotion = analysis_result[0]['label'] # Get the emotion from the analysis result
# Map emotion label from the model to our suggestion database
if emotion == "LABEL_0":
emotion = "NEGATIVE"
elif emotion == "LABEL_1":
emotion = "POSITIVE"
else:
emotion = "NEUTRAL"
st.write(f"Emotion detected: {emotion}")
# Step 3: Suggest activities, articles, and videos
resources = suggest_activity({emotion: 1})
# Display suggestions, articles, and videos
st.write("Suggestions:")
for suggestion in resources["suggestions"]:
st.write(f"- {suggestion}")
st.write("Articles:")
for article in resources["articles"]:
st.write(f"- [{article['title']}]({article['url']})")
st.write("Videos:")
for video in resources["videos"]:
st.write(f"- [{video['title']}]({video['url']})")
else:
st.write("Please answer all three questions to receive suggestions.")
if __name__ == "__main__":
main()
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