Create app.py

app.py

@@ -0,0 +1,71 @@
+import torch
+import gradio as gr
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    AutoModelForSequenceClassification
+)
+
+model_dir = "my-bert-model"
+
+config = AutoConfig.from_pretrained(
+    model_dir,
+    num_labels=3,
+    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.Row():
+        with gr.Column():
+            input_text = gr.Textbox(
+                placeholder="Insert your prompt here:",
+                scale=2,
+                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=True
+    )
+
+    examples = [
+        ["My last two weather pics from the storm on August 2nd. People packed up real fast after the temp dropped and winds picked up.", 1],
+        ["Lying Clinton sinking! Donald Trump singing: Let's Make America Great Again!", 0],
+    ]
+
+demo.queue()
+demo.launch()
+