Create emotion-app.py

emotion-app.py

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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.")