Update app.py

app.py

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+import shap
+import numpy as np
+import torch
+from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+import streamlit as st
+
+model_name = "mavinsao/mi-roberta-base-finetuned-mental-health"
+model = AutoModelForSequenceClassification.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Create a pipeline with the model and tokenizer
+classifier = pipeline("text-classification", model=model, tokenizer=tokenizer)
+
+# Streamlit app
+st.title("SHAP Explanation for Mental Illness Prediction")
+
+# Input text area for user input
+text = st.text_area("Enter a sentence to explain:")
+
+if st.button("Explain"):
+    # Generate the SHAP explainer
+    explainer = shap.Explainer(classifier, masker=tokenizer)
+
+    # Compute SHAP values
+    shap_values = explainer([text])
+
+    # Save SHAP plot as HTML
+    shap_html = shap.plots.text(shap_values, display=False)
+    
+    # Save the plot to an HTML file
+    shap_html.save_html("shap_plot.html")
+
+    # Read the HTML file and display in Streamlit
+    with open("shap_plot.html", "r") as f:
+        shap_html = f.read()
+
+    # Display the SHAP plot in Streamlit using components
+    st.components.v1.html(shap_html, height=500, scrolling=True)