Update app.py

app.py

@@ -2,32 +2,31 @@ import gradio as gr
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 
-# Load tokenizer and model
+# Load model and tokenizer
 tokenizer = AutoTokenizer.from_pretrained("BAAI/bge-reranker-v2-m3")
 model = AutoModelForSequenceClassification.from_pretrained("BAAI/bge-reranker-v2-m3")
 
-# Define reranking function
-def rerank(query, documents_text):
-    documents = documents_text.strip().split('\n')
-    pairs = [(query, doc) for doc in documents]
+# Reranking logic
+def rerank(query, docs_text):
+    docs = docs_text.strip().split('\n')
+    pairs = [(query, doc) for doc in docs]
     inputs = tokenizer(pairs, padding=True, truncation=True, return_tensors="pt")
     with torch.no_grad():
         scores = model(**inputs).logits.squeeze(-1)
-    results = sorted(zip(documents, scores.tolist()), key=lambda x: x[1], reverse=True)
-    output = "\n\n".join([f"Score: {score:.4f}\n{doc}" for doc, score in results])
-    return output
+    results = sorted(zip(docs, scores.tolist()), key=lambda x: x[1], reverse=True)
+    return "\n\n".join([f"Score: {score:.4f}\n{doc}" for doc, score in results])
 
-# Gradio Interface
+# Create API-ready Interface
 iface = gr.Interface(
     fn=rerank,
     inputs=[
-        gr.Textbox(label="Query", placeholder="Enter your search query", lines=1),
-        gr.Textbox(label="Documents (one per line)", placeholder="Enter one document per line", lines=10)
+        gr.Textbox(label="Query", lines=1),
+        gr.Textbox(label="Documents (one per line)", lines=10)
     ],
-    outputs=gr.Textbox(label="Reranked Output"),
+    outputs="text",
     title="BGE Reranker v2 M3",
-    description="Input a query and multiple documents. Returns reranked results with scores."
+    description="Rerank a list of documents based on a search query using BGE v2 M3."
 )
 
-# Launch the interface (no share=True needed)
+# ✅ Do NOT use `share=True`, do NOT set `ssr_mode`
 iface.launch()