Spaces:
Sleeping
Sleeping
| # 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 | |
| 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 | |
| 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.") | |