Upload app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import numpy as np
+
+hhem = pipeline("text-classification", model="vectara/hallucination_evaluation_model")
+
+def get_hhem_score(sentence1, sentence2):
+    output = hhem(f'{sentence1} [SEP] {sentence2}')
+    score = np.round(output[0]['score'], 4)
+
+    return score
+
+demo = gr.Interface(
+    fn=get_hhem_score,
+    inputs=[
+        gr.components.Textbox(label="Sentence 1"),
+        gr.components.Textbox(label="Sentence 2"),
+    ],
+    outputs=gr.components.Label(num_top_classes=1, label='HHEM Score'),
+    examples=[
+        ["Vectara provides RAG-as-a-service", "RAG-as-a-service is provided by Vectara"],
+        ["The cat sat on the mat.", "A feline was resting on a small rug."],
+        ["The quick brown fox jumps over the lazy dog.", "A fast red fox leaps across a sleepy canine."],
+    ],
+    cache_examples=False,
+    allow_flagging="never",
+    flagging_options=None,
+    title="HHEM Demo",
+    description="This demo uses Vectara's Hallucination Evaluation model (HHEM) to calculate factual consistency between two input sentences.",
+)
+
+# Launch the demo
+demo.launch()