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# Import necessary libraries
import streamlit as st
from transformers import pipeline
import torch
from datasets import load_dataset
# Load the T5-based Emotion Classifier model
@st.cache_resource
def load_model():
try:
st.write("Loading the emotion analysis model...")
emotion_analyzer = pipeline("text-classification", model="suryakiran786/T5-emotion")
st.write("Model loaded successfully!")
return emotion_analyzer
except Exception as e:
st.write(f"Error loading the model: {e}")
return None
# Initialize the model (with caching to prevent reloads)
emotion_analyzer = load_model()
# Load the dataset if needed for any additional logic
@st.cache_data
def load_data():
try:
# For demonstration purposes, let's load a sentiment analysis dataset from Hugging Face
dataset = load_dataset("glue", "sst2")
st.write("Dataset loaded successfully!")
return dataset
except Exception as e:
st.write(f"Error loading dataset: {e}")
return None
# Load data (just to show usage, not used in emotion analysis directly)
dataset = load_data()
# Function to predict emotion for a single response
def predict_emotion_single(response):
if not emotion_analyzer:
return {"Error": "Emotion analyzer model not initialized. Please check model loading."}
try:
response = response.strip()
result = emotion_analyzer([response])
return {res["label"]: round(res["score"], 4) for res in result}
except Exception as e:
return {"Error": str(e)}
# Streamlit App Layout
st.title("Behavior Prediction App")
st.write("Enter your thoughts or feelings, and let the app predict your emotional states.")
# Define questions for the user
questions = [
"How are you feeling today?",
"Describe your mood in a few words.",
"What was the most significant emotion you felt this week?",
"How do you handle stress or challenges?",
"What motivates you the most right now?"
]
# Initialize a dictionary to store responses
responses = {}
# Ask each question and get response
for i, question in enumerate(questions, start=1):
user_response = st.text_input(f"Question {i}: {question}")
if user_response:
analysis = predict_emotion_single(user_response)
responses[question] = (user_response, analysis)
st.write(f"**Your Response**: {user_response}")
st.write(f"**Emotion Analysis**: {analysis}")
# Provide button to clear input fields
if st.button("Clear Responses"):
st.experimental_rerun()
# Display results once all responses are filled
if st.button("Submit Responses"):
if responses:
st.write("-- Emotion Analysis Results ---")
for i, (question, (response, analysis)) in enumerate(responses.items(), start=1):
st.write(f"\n**Question {i}:** {question}")
st.write(f"Your Response: {response}")
st.write(f"Emotion Analysis: {analysis}")
else:
st.write("Please answer all the questions before submitting.")
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