Update app.py

app.py

@@ -2,22 +2,22 @@ import gradio as gr
 from sentence_transformers import CrossEncoder
 
 # Load the reranker model
-model = CrossEncoder("jinaai/jina-reranker-v2-base-multilingual", trust_remote_code=True )
+model = CrossEncoder("jinaai/jina-reranker-v2-base-multilingual", trust_remote_code=True)
 
-# Function to score query-document pairs
+# Function to rerank documents
 def rerank(query, documents):
-    scores = model.predict([[query, doc] for doc in documents])
+    documents = documents.split("\n")  # Split input into a list
+    scores = model.predict([[query, doc] for doc in documents if doc.strip()])
     ranked_docs = sorted(zip(documents, scores), key=lambda x: x[1], reverse=True)
     return [{"document": doc, "score": round(score, 4)} for doc, score in ranked_docs]
 
-# Define Gradio interface
+# Gradio Interface
 iface = gr.Interface(
     fn=rerank,
-    inputs=["text", gr.List(gr.Textbox())], 
+    inputs=["text", gr.Textbox(label="Documents (One per line)", lines=5, placeholder="Enter one document per line")], 
     outputs="json",
     title="JinaAI v2 Reranker API",
     description="Enter a query and a list of documents. The model will rank them based on relevance.",
 )
 
-# Launch Gradio app
 iface.launch()