Update app.py

app.py

@@ -38,19 +38,31 @@ model = ORTModelForSequenceClassification.from_pretrained(model_id)
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 
 # Set top_k=3 to get the top 3 results
-pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer, top_k=3)
+# Define the pipeline
+pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer)
 
 def classify_text(text):
-    results = pipe(text)
-    output = ""
+    start_time = time.time()
     
-    # Loop through up to 3 results
-    for i, result in enumerate(results[:3]):
-        # Each result should be a dictionary, so access it with ['label'] and ['score']
-        output += f"Label {i+1}: {result['label']}, Score: {result['score']:.4f}\n"
+    # Get results with all scores returned
+    results = pipe(text, return_all_scores=True)
+    
+    end_time = time.time()
 
+    output = f"Sentence: {text}\n"
+    
+    # Sort results by score in descending order
+    sorted_results = sorted(results[0], key=lambda x: x['score'], reverse=True)
+    
+    # Print the top 3 highest-scoring labels and scores
+    for i, result in enumerate(sorted_results[:3]):  # Limiting to the top 3 results
+        output += f"Label {i+1}: {result['label']}, Score: {result['score']:.4f}\n"
+    
+    output += f"Generation time: {end_time - start_time:.2f} seconds\n"
+    
     return output
 
+# Gradio Interface
 gr.Interface(
     fn=classify_text,
     title="Sentiment Classifier",
@@ -66,4 +78,4 @@ gr.Interface(
         ["I am deeply disappointed in your bad performance in last league match loss, and quite disappointed, sad because of it."],
         ["I am very happy with your excellent performance!"]
     ]
-).launch()
+).launch()