Create app.py

app.py

@@ -0,0 +1,52 @@
+import string
+import gradio as gr
+import requests
+import torch
+from transformers import (
+    AutoConfig,
+    AutoModelForSequenceClassification,
+    AutoTokenizer,
+)
+
+model_dir = "my-bert-model"
+
+config = AutoConfig.from_pretrained(model_dir, num_labels=2, finetuning_task="text-classification")
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForSequenceClassification.from_pretrained(model_dir, config=config)
+
+def inference(input_text):
+    inputs = tokenizer.batch_encode_plus(
+                [input_text],
+                max_length=512,
+                pad_to_max_length=True,
+                truncation=True,
+                padding="max_length",
+                return_tensors="pt",
+            )
+    
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    
+    predicted_class_id = logits.argmax().item()
+    output = model.config.id2label[predicted_class_id]
+    return output
+
+with gr.Blocks(css="""
+    .message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 20px; margin-top: 20px}
+    #component-21 > div.wrap.svelte-w6rprc {height: 600px;}
+    """) as demo:
+    with gr.Column(elem_id="container"):
+        with gr.Row():
+            with gr.Row():
+                input_text = gr.Textbox(
+                    placeholder="Insert your prompt here:", scale=5, container=False
+                )
+                answer = gr.Textbox(lines=0, label="Answer")
+                generate_bt = gr.Button("Generate", scale=1)
+        inputs = [input_text]
+        outputs = [answer]
+        generate_bt.click(
+            fn=inference, inputs=inputs, outputs=outputs, show_progress=False
+        )
+demo.queue()
+demo.launch()