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import streamlit as st
import os
# Suggestion Database
suggestion_database = {
"sadness": {
"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"},
],
},
"joy": {
"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
def get_relevant_resources(emotion):
return suggestion_database.get(emotion, {"suggestions": [], "articles": [], "videos": []})
# Debugging Model Initialization
def load_emotion_model(model_path="path/to/model"):
if not os.path.exists(model_path):
st.error(f"Model file not found at {model_path}")
return None
try:
# Replace with actual model loading logic
model = "Dummy model loaded"
st.success("Model loaded successfully!")
return model
except Exception as e:
st.error(f"Error loading model: {e}")
return None
# Analyze User Input and Provide Suggestions
def analyze_emotion(user_input, model):
if model is None:
return "neutral" # Default to neutral if model failed to load
try:
# Dummy emotion analysis (replace with model prediction logic)
if "sad" in user_input.lower():
return "sadness"
elif "happy" in user_input.lower() or "joy" in user_input.lower():
return "joy"
else:
return "neutral"
except Exception as e:
st.error(f"Error during emotion analysis: {e}")
return "neutral"
# Streamlit Interface
def main():
st.title("Emotion-Based Suggestions")
# Load Model
st.sidebar.title("Model Loader")
model_path = st.sidebar.text_input("Model Path", "path/to/model")
model = load_emotion_model(model_path)
# User Input
st.header("How are you feeling today?")
user_input = st.text_input("Describe your mood in a few words:")
if user_input:
# Analyze Emotion
emotion = analyze_emotion(user_input, model)
st.subheader(f"Detected Emotion: {emotion.capitalize()}")
# Fetch and Display Suggestions
resources = get_relevant_resources(emotion)
st.subheader("Suggestions for You:")
for suggestion in resources["suggestions"]:
st.write(f"- {suggestion}")
st.subheader("Articles to Explore:")
for article in resources["articles"]:
st.write(f"- [{article['title']}]({article['url']})")
st.subheader("Videos to Watch:")
for video in resources["videos"]:
st.write(f"- [{video['title']}]({video['url']})")
# Run the App
if __name__ == "__main__":
main()
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