Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from transformers import TabularTransformerForSequenceClassification, TabularTransformerConfig
+from transformers import Trainer, TrainingArguments
+from datasets import Dataset
+import torch
+
+# Sample Data
+data = {
+    'feature1': [0.5, 0.3, 0.7, 0.2],
+    'feature2': [1, 0, 1, 1],
+    'feature3': [0.6, 0.1, 0.8, 0.4],
+    'label': [0, 1, 0, 1]  # Binary classification
+}
+df = pd.DataFrame(data)
+dataset = Dataset.from_pandas(df)
+
+# Configure the Model
+config = TabularTransformerConfig(
+    num_labels=2,  # Binary classification
+    numerical_features=['feature1', 'feature2', 'feature3']
+)
+model = TabularTransformerForSequenceClassification(config)
+
+# Define Training Arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=4,
+    num_train_epochs=3
+)
+
+# Define Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=dataset,
+    eval_dataset=dataset
+)
+
+# Train the model
+trainer.train()
+
+# Define Inference Function
+def classify(feature1, feature2, feature3):
+    input_data = {'feature1': feature1, 'feature2': feature2, 'feature3': feature3}
+    input_df = pd.DataFrame([input_data])
+    test_dataset = Dataset.from_pandas(input_df)
+    with torch.no_grad():
+        logits = model(**test_dataset[:][0]).logits
+        prediction = torch.argmax(logits, dim=1).item()
+        return "Class 1" if prediction == 1 else "Class 0"
+
+# Gradio Interface
+iface = gr.Interface(
+    fn=classify,
+    inputs=[
+        gr.inputs.Slider(0, 1, step=0.1, label="Feature 1"),
+        gr.inputs.Slider(0, 1, step=0.1, label="Feature 2"),
+        gr.inputs.Slider(0, 1, step=0.1, label="Feature 3")
+    ],
+    outputs="text",
+    title="Tabular Classification with Hugging Face",
+    description="Classify entries based on tabular data"
+)
+
+iface.launch()