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b1bca3c d54d1ef 2c8fd9e 6afd016 d54d1ef b1bca3c d54d1ef | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 | import streamlit as st
from transformers import pipeline
import random
import re
from textblob import TextBlob
# 1) Load a pretrained emotion classifier
@st.cache(allow_output_mutation=True)
def load_emotion_pipeline():
return pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
clf = load_emotion_pipeline()
# 2) Your response logic (from your script)
responses = {
"sadness": [
"It’s okay to feel down sometimes. I’m here to support you.",
"I'm really sorry you're going through this. Want to talk more about it?",
"You're not alone — I’m here for you."
],
"anger": [
"That must have been frustrating. Want to vent about it?",
"It's okay to feel this way. I'm listening.",
"Would it help to talk through it?"
],
"love": [
"That’s beautiful to hear! What made you feel that way?",
"It’s amazing to experience moments like that.",
"Sounds like something truly meaningful."
],
"joy": [ # note: pipeline uses 'joy' instead of 'happiness'
"That's awesome! What’s bringing you joy today?",
"I love hearing good news. 😊",
"Yay! Want to share more about it?"
],
"surprise": [
"Oh! That’s surprising. Tell me more!",
"Wow, that’s unexpected!",
"Sounds like something caught you off guard."
],
"fear": [
"I hear your concern. 😟",
"It’s okay to feel anxious sometimes.",
"I’m here with you."
],
"disgust": [
"That sounds upsetting. I'm here to listen.",
"I understand that’s hard to hear.",
"Let’s talk through what’s bothering you."
],
"neutral": [
"Got it. I’m here if you want to dive deeper.",
"Thanks for sharing that. Tell me more if you’d like.",
"I’m listening. How else can I support you?"
]
}
help_keywords = ["suggest","help","calm","exercise","relax","any tips","can you"]
bye_inputs = ["bye","goodbye","exit","quit"]
thank_you_inputs= ["thank","thanks","thank you"]
awaiting_tip = False
relaxation_resources = {
"exercise": "Try the 5-4-3-2-1 grounding method:\n- 5 things you see\n- 4 you can touch\n- 3 you hear\n- 2 you smell\n- 1 you taste",
"video": "Here’s a short calming video: https://youtu.be/O-6f5wQXSu8"
}
# 3) Streamlit UI
st.title("🌿 EmotiBot")
user_input = st.text_input("You:")
if st.button("Send") and user_input:
# Optional: spelling correction
user = str(TextBlob(user_input).correct())
# Exit
if any(b in user.lower() for b in bye_inputs):
st.write("EmotiBot 🌿: Take care! I’m here whenever you want to talk.")
# Thank you
elif any(t in user.lower() for t in thank_you_inputs):
st.write("EmotiBot 🌿: You're most welcome! 💙")
# Help keywords → ask tip
elif any(k in user.lower() for k in help_keywords):
awaiting_tip = True
st.write("EmotiBot 🌿: Would you prefer a short calming video or a quick breathing exercise?")
# Tip follow-up
elif awaiting_tip:
choice = "video" if "video" in user.lower() else "exercise"
response = relaxation_resources.get(choice, relaxation_resources["exercise"])
st.write(f"EmotiBot 🌿: {response}")
awaiting_tip = False
else:
# 4) Emotion inference
pred = clf(user)[0]["label"].lower()
# Map pipeline labels to your keys
pred = "happiness" if pred == "joy" else pred
st.write(f"**Detected emotion:** {pred}")
reply = random.choice(responses.get(pred, responses["neutral"]))
st.write(f"EmotiBot 🌿: {reply}")
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