Create app.py

app.py

@@ -0,0 +1,58 @@
+import time
+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 = """
+
+# 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"
+
+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"
+    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",
+    inputs=gr.Textbox(
+        label="Input Text",
+        placeholder="Type something here..."
+    ),
+    outputs=gr.Textbox(
+        label="Classification Results"
+    ),
+    examples=[
+        ["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()