Update app.py

app.py

@@ -4,28 +4,43 @@ import torch
 
 # Load model and tokenizer
 model_name = "cross-encoder/ms-marco-MiniLM-L-12-v2"
+
+print("Loading model and tokenizer...")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 model = AutoModelForSequenceClassification.from_pretrained(model_name)
+model.eval()  # Set model to evaluation mode
+print("Model and tokenizer loaded successfully.")
 
 # Function to compute relevance score
 def get_relevance_score(query, paragraph):
+    if not query.strip() or not paragraph.strip():
+        return "Please provide both a query and a document paragraph."
+    
+    print(f"Received inputs -> Query: {query}, Paragraph: {paragraph}")
+
+    # Tokenize inputs
     inputs = tokenizer(query, paragraph, return_tensors="pt", truncation=True, padding=True)
-    model.eval()
+
+    # Perform inference without gradient tracking
     with torch.no_grad():
-        scores = model(**inputs).logits.squeeze().item()
-    return round(scores, 4)
+        score = model(**inputs).logits.squeeze().item()
+    
+    print(f"Calculated score: {score}")
+    return round(score, 4)
 
-# Gradio interface
+# Define Gradio interface
 interface = gr.Interface(
     fn=get_relevance_score,
     inputs=[
         gr.Textbox(label="Query", placeholder="Enter your search query..."),
         gr.Textbox(label="Document Paragraph", placeholder="Enter a paragraph to match...")
     ],
-    outputs=gr.Number(label="Relevance Score"),
+    outputs=gr.Textbox(label="Relevance Score"),
     title="Cross-Encoder Relevance Scoring",
-    description="Enter a query and a document paragraph to get a relevance score using the MS MARCO MiniLM L-12 v2 model."
+    description="Enter a query and a document paragraph to get a relevance score using the MS MARCO MiniLM L-12 v2 model.",
+    allow_flagging="never"
 )
 
 if __name__ == "__main__":
+    print("Launching Gradio app...")
     interface.launch()