Update app.py

app.py

@@ -23,15 +23,9 @@ install_packages()
 
 import gradio as gr
 from huggingface_hub import login
-from optimum.onnxruntime import ORTModelForSeq2SeqLM
-from transformers import AutoTokenizer, pipeline
-
-from transformers import AutoTokenizer, pipeline
 from optimum.onnxruntime import ORTModelForSequenceClassification
-import gradio as gr
-# with gr.Blocks(css=".gradio-container {background-color: red}") as demo:# with gr.Blocks(css=".gradio-container {background: url('file=clouds.jpg')}") as demo:# css = """
+from transformers import AutoTokenizer, pipeline
 
-# with gr.Blocks(css=css) as demo:#     box1 = gr.Textbox(value="Good Job", elem_classes="feedback")#     box2 = gr.Textbox(value="Failure", elem_id="warning", elem_classes="feedback")
 model_id = "HassamAliCADI/SentimentOnx"
 hf_token = os.environ.get("NLP")
 
@@ -42,38 +36,24 @@ else:
 
 model = ORTModelForSequenceClassification.from_pretrained(model_id)
 tokenizer = AutoTokenizer.from_pretrained(model_id)
-# with gr.Blocks(css=".gradio-container {background-color: red}") as demo:# with gr.Blocks(css=".gradio-container {background: url('file=clouds.jpg')}") as demo:# css = """
 
-# with gr.Blocks(css=css) as demo:#     box1 = gr.Textbox(value="Good Job", elem_classes="feedback")#     box2 = gr.Textbox(value="Failure", elem_id="warning", elem_classes="feedback")
 pipe = pipeline(task="text-classification", model=model, tokenizer=tokenizer)
 
 def classify_text(text):
-  #  start_time = time.time()
     results = pipe(text)
- #   end_time = time.time()
-
-# with gr.Blocks(css=".gradio-container {background-color: red}") as demo:# with gr.Blocks(css=".gradio-container {background: url('file=clouds.jpg')}") as demo:# css = """
-
-# with gr.Blocks(css=css) as demo:#     box1 = gr.Textbox(value="Good Job", elem_classes="feedback")#     box2 = gr.Textbox(value="Failure", elem_id="warning", elem_classes="feedback")
-
-
-# #warning {background-color: #FFCCCB}# .feedback textarea {font-size: 24px !important}# """
-    
     output = ""
-    for result in results:
-        output += f"Label: {result['label']}, Score: {result['score']:.4f}\n"
-   # output += f"\nGeneration time: {end_time - start_time:.2f} seconds"
+    
+    if len(results) > 0:
+        # Print the first result
+        output += f"Label 1: {result['label']}, Score: {result['score']:.4f}\n"
+        
+        # Print the second result if it exists
+        if len(results) > 1:
+            output += f"Label 2: {results[1]['label']}, Score: {results[1]['score']:.4f}\n"
+
     return output
 
 gr.Interface(
-
-# with gr.Blocks(css=".gradio-container {background-color: red}") as demo:# with gr.Blocks(css=".gradio-container {background: url('file=clouds.jpg')}") as demo:# css = """
-
-# with gr.Blocks(css=css) as demo:#     box1 = gr.Textbox(value="Good Job", elem_classes="feedback")#     box2 = gr.Textbox(value="Failure", elem_id="warning", elem_classes="feedback")
-
-
-# #warning {background-color: #FFCCCB}# .feedback textarea {font-size: 24px !important}# """
-    
     fn=classify_text,
     title="Sentiment Classifier",
     description="Enter text to classify sentiment",