Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+from scripts.evaluation import input_classification
+from scripts.explainer import CustomExplainer
+import numpy as np
+
+checkpoint = "Detsutut/medbit-assertion-negation"
+model = AutoModelForSequenceClassification.from_pretrained(checkpoint, num_labels=3, local_files_only=True)
+tokenizer = AutoTokenizer.from_pretrained(checkpoint, local_files_only=True)
+cls_explainer = CustomExplainer(model, tokenizer)
+sentence = "Il paziente non mostra alcun segno di [entità]."
+
+
+def compute(text):
+    output = input_classification(model=model, tokenizer=tokenizer, x=text, all_classes=True)
+    exp = cls_explainer(text)
+    entities = []
+    start = 0
+    words_and_exp = cls_explainer.merge_attributions(exp)
+    low_threshold = np.percentile([float(abs(w)) for _, w in words_and_exp], 25)
+    high_threshold = np.percentile([float(abs(w)) for _, w in words_and_exp], 75)
+    for i, entity in enumerate(words_and_exp):
+        if entity[1] < 0 and entity[1] < low_threshold:
+            polarity = "-"
+            entities.append({"entity": polarity, "start": start, "end": start + len(entity[0])})
+        elif entity[1] > 0 and entity[1] > high_threshold:
+            polarity = "+"
+            entities.append({"entity": polarity, "start": start, "end": start + len(entity[0])})
+        start = start + len(entity[0]) + 1
+    return output, gr.HighlightedText(label="Explanation", visible=True, color_map={"+": "green", "-": "red"},
+                                      value={"text": " ".join([e[0] for e in words_and_exp]), "entities": entities},
+                                      combine_adjacent=True, adjacent_separator=" ")
+
+
+with gr.Blocks(title="Inference GUI") as gui:
+    text = gr.Textbox(label="Input Text", value=sentence)
+    explanation = gr.HighlightedText(label="Explanation", visible=False)
+    output = gr.Label(label="Predicted Label", num_top_classes=3)
+    compute_btn = gr.Button("Predict")
+    compute_btn.click(fn=compute, inputs=text, outputs=[output, explanation], api_name="compute")
+
+gui.launch()