Create app.py

app.py

@@ -0,0 +1,31 @@
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import torch.nn.functional as F
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-v2-m3")
+model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-v2-m3")
+
+# Define a function to get score
+def rerank_score(query, document):
+    # Format input as "query: document"
+    input_text = f"{query} [SEP] {document}"
+    inputs = tokenizer(input_text, return_tensors="pt", truncation=True, padding=True)
+    with torch.no_grad():
+        outputs = model(**inputs)
+        scores = F.softmax(outputs.logits, dim=1)
+    relevance_score = scores[0][1].item()  # Assuming label 1 means "relevant"
+    return f"Relevance Score: {relevance_score:.4f}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=rerank_score,
+    inputs=[gr.Textbox(label="Query"), gr.Textbox(label="Document")],
+    outputs=gr.Textbox(label="Relevance Score"),
+    title="BAAI bge-reranker-v2-m3",
+    description="Enter a query and a document to get a relevance score."
+)
+
+if __name__ == "__main__":
+    demo.launch()