added sentiment predictions

app.py

@@ -2,72 +2,141 @@ import os
 import torch
 import streamlit as st
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from transformers import pipeline
+from plotly.subplots import make_subplots
 import plotly.graph_objects as go
 
-input_text = st.text_input(
-    label='Estimate item desirability:',
-    value='I love a good fight.',
-    placeholder='Enter item'
-
-)
-
-#model_path = '/nlp/nlp/models/finetuned/twitter-xlm-roberta-base-regressive-desirability-ft-4'
-model_path = 'magnolia-psychometrics/item-desirability'
-
-#auth_token = os.environ.get("item-desirability") or True
-auth_token = "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"
-
-tokenizer = AutoTokenizer.from_pretrained(
-    pretrained_model_name_or_path=model_path,
-    use_fast=True,
-    use_auth_token=auth_token
-)
-
-model = AutoModelForSequenceClassification.from_pretrained(
-    pretrained_model_name_or_path=model_path, 
-    num_labels=1, 
-    ignore_mismatched_sizes=True,
-    use_auth_token=auth_token
-)
 
 def z_score(y, mean=.04853076, sd=.9409466):
     return (y - mean) / sd
 
-if input_text:    
-    
-    inputs = tokenizer(input_text, padding=True, return_tensors='pt')
-
-    with torch.no_grad():
-        score = model(**inputs).logits.squeeze().tolist()
-        z = z_score(score)
+def indicator_plot(value, title, value_range, domain):
 
-    fig = go.Figure(go.Indicator(
+    plot = go.Indicator(
         mode = "gauge+delta",
-        value = z,
-        domain = {'x': [0, 1], 'y': [0, 1]},
-        title = f"Item Desirability <br><sup>\"{input_text}\"</sup>",
+        value = value,
+        domain = domain,
+        title = title,
         delta = {
             'reference': 0, 
             'decreasing': {'color': "#ec4899"},
             'increasing': {'color': "#36def1"}
             },
         gauge = {
-            'axis': {'range': [-4, 4], 'tickwidth': 1, 'tickcolor': "black"},
+            'axis': {'range': value_range, 'tickwidth': 1, 'tickcolor': "black"},
             'bar': {'color': "#4361ee"},
             'bgcolor': "white",
             'borderwidth': 2,
             'bordercolor': "#efefef",
             'steps': [
-                {'range': [-4, 0], 'color': '#efefef'},
-                {'range': [0, 4], 'color': '#efefef'}],
+                {'range': [value_range[0], 0], 'color': '#efefef'},
+                {'range': [0, value_range[1]], 'color': '#efefef'}
+            ],
             'threshold': {
                 'line': {'color': "#4361ee", 'width': 8},
                 'thickness': 0.75,
-                'value': z}
-            }))
+                'value': value
+            }
+        }
+    )
+
+    return plot
+
+body = """
+    # NLP for Item Desirability Ratings
+    This web application accompanies the paper *Leveraging Natural Language Processing for Item Desirability Ratings: 
+    A Machine-Based Alternative to Human Judges* submitted to the Journal *Personality and Individual Differences*.
+
+    ## What is this research about?
+    Researchers use personality scales to measure people's traits and behaviors, but biases can affect the accuracy of these scales. 
+    Socially desirable responding is a common bias that can skew results. To overcome this, researchers gather item desirability ratings, e.g., to ensure that questions are neutral. 
+    Recently, advancements in natural language processing have made it possible to use machines to estimate social desirability ratings, 
+    which can provide a viable alternative to human ratings and help researchers, scale developers, and practitioners improve the accuracy of personality scales.
+
+    ## Try it yourself!
+    Use the text field below to enter a statement that might be part of a psychological questionnaire (e.g., "I love a good fight.").
+    The left dial will indicate how socially desirable it might be to endorse this item. 
+    The right dial indicates sentiment (i.e., valence) as estimated by regular sentiment analysis (using the `cardiffnlp/twitter-xlm-roberta-base-sentiment` model).    
+"""
+
+st.markdown(body)
+
+input_text = st.text_input(
+    label='Estimate item desirability:',
+    value='I love a good fight.',
+    placeholder='Enter item'
+)
+
+# desirability model
+model_path = '/nlp/nlp/models/finetuned/twitter-xlm-roberta-base-regressive-desirability-ft-4'
+#model_path = 'magnolia-psychometrics/item-desirability'
+#auth_token = os.environ.get("item-desirability") or True
+auth_token = "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"
+
+
+if 'tokenizer' not in globals():
+    tokenizer = AutoTokenizer.from_pretrained(
+        pretrained_model_name_or_path=model_path,
+        use_fast=True,
+        use_auth_token=auth_token
+    )
+
+if 'model' not in globals():
+    model = AutoModelForSequenceClassification.from_pretrained(
+        pretrained_model_name_or_path=model_path, 
+        num_labels=1, 
+        ignore_mismatched_sizes=True,
+        use_auth_token=auth_token
+    )
+
+# sentiment classifier
+if 'classifier' not in globals():
+    sentiment_model = 'cardiffnlp/twitter-xlm-roberta-base-sentiment'
+    classifier = pipeline("sentiment-analysis", model=sentiment_model, tokenizer=sentiment_model, top_k=3)
+
+classifier_output = classifier(input_text)
+classifier_output_dict = {x['label']: x['score'] for x in classifier_output[0]}
+classifier_score = classifier_output_dict['positive'] - classifier_output_dict['negative']
+
+if input_text:    
+    
+    inputs = tokenizer(input_text, padding=True, return_tensors='pt')
+
+    with torch.no_grad():
+        score = model(**inputs).logits.squeeze().tolist()
+        z = z_score(score)
+
+    p1 = indicator_plot(
+        value=z,
+        title=f"Item Desirability",
+        value_range=[-4, 4],
+        domain={'x': [0, .45], 'y': [0, 1]},    
+    )
+
+    p2 = indicator_plot(
+        value=classifier_score,
+        title=f"Item Sentiment",
+        value_range=[-1, 1],
+        domain={'x': [.55, 1], 'y': [0, 1]}
+        
+    )
+
+    fig = go.Figure()    
+    fig.add_trace(p1)
+    fig.add_trace(p2)
 
     fig.update_layout(
+        title=dict(text=f'"{input_text}"', font=dict(size=36),yref='paper'),
         paper_bgcolor = "white", 
         font = {'color': "black", 'family': "Arial"})
         
     st.plotly_chart(fig, theme=None, use_container_width=True)
+    
+
+    notes = """
+    Item desirability: z-transformed values, 0 indicated "neutral".
+
+    Item sentiment: Absolute differences between positive and negative sentiment.
+    """
+
+    st.markdown(notes)