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README.md

@@ -1,12 +1,12 @@
+
 ---
-title: Blocks Interpretation 3-x
-emoji: 💻
-colorFrom: blue
-colorTo: gray
+title: blocks_interpretation_3-x 
+emoji: 🔥
+colorFrom: indigo
+colorTo: indigo
 sdk: gradio
-sdk_version: 4.3.0
-app_file: app.py
+sdk_version: 3.50.1
+app_file: run.py
 pinned: false
+hf_oauth: true
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

requirements.txt

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+shap
+matplotlib
+transformers
+torch

run.ipynb

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+{"cells": [{"cell_type": "markdown", "id": "302934307671667531413257853548643485645", "metadata": {}, "source": ["# Gradio Demo: blocks_interpretation"]}, {"cell_type": "code", "execution_count": null, "id": "272996653310673477252411125948039410165", "metadata": {}, "outputs": [], "source": ["!pip install -q gradio shap matplotlib transformers torch"]}, {"cell_type": "code", "execution_count": null, "id": "288918539441861185822528903084949547379", "metadata": {}, "outputs": [], "source": ["import gradio as gr\n", "import shap\n", "from transformers import pipeline\n", "import matplotlib.pyplot as plt\n", "\n", "\n", "sentiment_classifier = pipeline(\"text-classification\", return_all_scores=True)\n", "\n", "\n", "def classifier(text):\n", "    pred = sentiment_classifier(text)\n", "    return {p[\"label\"]: p[\"score\"] for p in pred[0]}\n", "\n", "\n", "def interpretation_function(text):\n", "    explainer = shap.Explainer(sentiment_classifier)\n", "    shap_values = explainer([text])\n", "    # Dimensions are (batch size, text size, number of classes)\n", "    # Since we care about positive sentiment, use index 1\n", "    scores = list(zip(shap_values.data[0], shap_values.values[0, :, 1]))\n", "\n", "    scores_desc = sorted(scores, key=lambda t: t[1])[::-1]\n", "\n", "    # Filter out empty string added by shap\n", "    scores_desc = [t for t in scores_desc if t[0] != \"\"]\n", "\n", "    fig_m = plt.figure()\n", "    plt.bar(x=[s[0] for s in scores_desc[:5]],\n", "            height=[s[1] for s in scores_desc[:5]])\n", "    plt.title(\"Top words contributing to positive sentiment\")\n", "    plt.ylabel(\"Shap Value\")\n", "    plt.xlabel(\"Word\")\n", "    return {\"original\": text, \"interpretation\": scores}, fig_m\n", "\n", "\n", "with gr.Blocks() as demo:\n", "    with gr.Row():\n", "        with gr.Column():\n", "            input_text = gr.Textbox(label=\"Input Text\")\n", "            with gr.Row():\n", "                classify = gr.Button(\"Classify Sentiment\")\n", "                interpret = gr.Button(\"Interpret\")\n", "        with gr.Column():\n", "            label = gr.Label(label=\"Predicted Sentiment\")\n", "        with gr.Column():\n", "            with gr.Tab(\"Display interpretation with built-in component\"):\n", "                interpretation = gr.components.Interpretation(input_text)\n", "            with gr.Tab(\"Display interpretation with plot\"):\n", "                interpretation_plot = gr.Plot()\n", "\n", "    classify.click(classifier, input_text, label)\n", "    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])\n", "\n", "if __name__ == \"__main__\":\n", "    demo.launch()"]}], "metadata": {}, "nbformat": 4, "nbformat_minor": 5}

run.py

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+import gradio as gr
+import shap
+from transformers import pipeline
+import matplotlib.pyplot as plt
+
+
+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.Tab("Display interpretation with built-in component"):
+                interpretation = gr.components.Interpretation(input_text)
+            with gr.Tab("Display interpretation with plot"):
+                interpretation_plot = gr.Plot()
+
+    classify.click(classifier, input_text, label)
+    interpret.click(interpretation_function, input_text, [interpretation, interpretation_plot])
+
+if __name__ == "__main__":
+    demo.launch()