Create app.py

app.py

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+import gradio as gr
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForQuestionAnswering, TrainingArguments, Trainer, pipeline
+
+# Load your dataset
+dataset = load_dataset("your-username/your-dataset-name")
+
+# Load a pre-trained model and tokenizer
+model_name = "distilbert-base-uncased-distilled-squad"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForQuestionAnswering.from_pretrained(model_name)
+
+# Tokenize the dataset
+def tokenize_function(examples):
+    return tokenizer(examples["question"], examples["text"], truncation=True, padding="max_length")
+
+tokenized_datasets = dataset.map(tokenize_function, batched=True)
+
+# Set up training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    evaluation_strategy="epoch",
+    learning_rate=2e-5,
+    per_device_train_batch_size=16,
+    per_device_eval_batch_size=16,
+    num_train_epochs=3,
+    weight_decay=0.01,
+)
+
+# Create Trainer instance
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    train_dataset=tokenized_datasets["train"],
+    eval_dataset=tokenized_datasets["validation"],
+)
+
+# Fine-tune the model
+trainer.train()
+
+# Save the model
+model.save_pretrained("./fine_tuned_model")
+
+# Create a question-answering pipeline
+qa_pipeline = pipeline("question-answering", model="./fine_tuned_model")
+
+# Define the Gradio interface function
+def answer_question(context, question):
+    result = qa_pipeline(question=question, context=context)
+    return result['answer']
+
+# Create and launch the Gradio interface
+iface = gr.Interface(
+    fn=answer_question,
+    inputs=["text", "text"],
+    outputs="text",
+    title="Textbook Q&A",
+    description="Ask a question about your textbook!"
+)
+
+iface.launch()