Create app.py

app.py

@@ -0,0 +1,42 @@
+import gradio as gr
+from transformers import AutoModelForSequenceClassification
+
+# Load the pre-trained model
+model = AutoModelForSequenceClassification.from_pretrained(
+    'jinaai/jina-reranker-v2-base-multilingual',
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+
+model.eval()
+
+def compute_scores(query, documents):
+    """
+    Compute scores between a query and multiple documents using the loaded model.
+    
+    Args:
+        query (str): The input query string.
+        documents (list of str): List of document strings to compare against the query.
+        
+    Returns:
+        list of float: Scores representing the relevance of each document to the query.
+    """
+    sentence_pairs = [[query, doc] for doc in documents]
+    scores = model.compute_score(sentence_pairs, max_length=1024)
+    return scores.tolist()
+
+# Define Gradio interface
+iface = gr.Interface(
+    fn=compute_scores,
+    inputs=[
+        gr.inputs.Textbox(lines=2, placeholder="Enter your query here..."),
+        gr.inputs.Textbox(lines=8, placeholder="Enter your documents separated by newlines...")
+    ],
+    outputs="json",
+    title="Sentence Pair Scoring with Jina Reranker Model",
+    description="This tool computes the relevance scores between a given query and a set of documents using the Jina Reranker model."
+)
+
+# Launch the interface
+if __name__ == "__main__":
+    iface.launch()