Add app file

app.py

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+import gradio as gr
+import shap
+from transformers import pipeline
+import matplotlib
+import matplotlib.pyplot as plt
+matplotlib.use('Agg')
+
+
+sentiment_classifier = pipeline("text-classification", return_all_scores=True)
+
+
+def classifier(text):
+    pred = sentiment_classifier(text)
+    return {p["label"]: p["score"] for p in pred[0]}
+
+
+def interpretation_function(text):
+    explainer = shap.Explainer(sentiment_classifier)
+    shap_values = explainer([text])
+    # Dimensions are (batch size, text size, number of classes)
+    # Since we care about positive sentiment, use index 1
+    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))
+
+    scores_desc = sorted(scores, key=lambda t: t[1])[::-1]
+
+    # Filter out empty string added by shap
+    scores_desc = [t for t in scores_desc if t[0] != ""]
+
+    fig_m = plt.figure()
+    plt.bar(x=[s[0] for s in scores_desc[:5]],
+            height=[s[1] for s in scores_desc[:5]])
+    plt.title("Top words contributing to positive sentiment")
+    plt.ylabel("Shap Value")
+    plt.xlabel("Word")
+    return {"original": text, "interpretation": scores}, fig_m
+
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text")
+            with gr.Row():
+                classify = gr.Button("Classify Sentiment")
+                interpret = gr.Button("Interpret")
+        with gr.Column():
+            label = gr.Label(label="Predicted Sentiment")
+        with gr.Column():
+            with gr.Tabs():
+                with gr.TabItem("Display interpretation with built-in component"):
+                    interpretation = gr.components.Interpretation(input_text)
+                with gr.TabItem("Display interpretation with plot"):
+                    interpretation_plot = gr.Plot()
+
+    classify.click(classifier, input_text, label)
+    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])
+
+demo.launch()

requirements.txt

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+torch
+transformers==4.16.2
+shap