Create app.py

app.py

@@ -0,0 +1,41 @@
+import transformers
+from transformers import AutoConfig, AutoTokenizer, AutoModelForSequenceClassification
+import torch
+import gradio as gr
+
+model_dir = "./experiments/checkpoint-382"
+
+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], return_tensors="pt", padding=True, max_length=512, truncation=True, padding="max_length")
+
+    with torch.no_grad():
+        logits = model(**inputs)["logits"]
+
+    predicted_class = torch.argmax(logits, dim=1).item()
+    output = model.config.id2label[predicted_class]
+    return output
+
+with gr.Blocks(css=""".message.svelte-w6rprc.svelte-w6rprc.svelte-w6rprc {font-size: 1.5em; padding: 1em; text-align: center;} #component-21 > div.wrap.svelte-w6rprc {height: 600px;}""") as demo:
+    with gr.Row():
+        with gr.Column():
+            input_text = gr.Textbox(label="Input Text", scale=2, container=False)
+            answer = gr.Output(label="Output", lines=0)
+            generate_btn = gr.Button(text="Generate", type="primary", scale=2)
+
+    inputs = [input_text]
+    outputs = [answer]
+
+    generate_btn.click(fn=inference, inputs=inputs, outputs=outputs, show_progress=True)
+
+    examples = [
+        ["I love this movie!"],
+        ["I hate this movie!"],
+        ["I feel neutral about this movie!"]
+    ]
+
+demo.queue()
+demo.launch()