Create app.py

app.py

@@ -0,0 +1,122 @@
+import gradio as gr
+from datasets import load_dataset
+import evaluate
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, DataCollatorForSeq2Seq, Seq2SeqTrainingArguments, Seq2SeqTrainer
+import numpy as np
+import nltk
+
+nltk.download("punkt")
+raw_dataset = load_dataset("scientific_papers", "pubmed")
+metric = evaluate.load("rouge")
+model_checkpoint = "t5-base"
+tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
+
+if model_checkpoint in ["t5-small", "t5-base", "t5-large", "t5-3b", "t5-11b"]:
+    prefix = "summarize: "
+else:
+    prefix = ""
+
+# preprocessing function
+max_input_length = 256
+max_target_length = 64
+def preprocess_function(examples):
+    inputs = [prefix + doc for doc in examples["article"]]
+    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True)
+
+    # Setup the tokenizer for targets
+    # with tokenizer.as_target_tokenizer():
+    labels = tokenizer(text_target=examples["abstract"], max_length=max_target_length, truncation=True)
+
+    model_inputs["labels"] = labels["input_ids"]
+    return model_inputs
+
+for split in ["train", "validation", "test"]:
+    raw_dataset[split] = raw_dataset[split].select([n for n in np.random.randint(0, len(raw_dataset[split]) - 1, 200)])
+
+tokenized_dataset = raw_dataset.map(preprocess_function, batched=True)
+
+model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
+
+batch_size = 4
+
+args = Seq2SeqTrainingArguments(
+    f"{model_checkpoint}-scientific_papers",
+    evaluation_strategy="epoch",
+    learning_rate=3e-5,
+    per_device_train_batch_size=batch_size,
+    per_device_eval_batch_size=batch_size,
+    weight_decay=0.01,
+    save_total_limit=3,
+    num_train_epochs=0.5,
+    predict_with_generate=True,
+    # fp16=True,
+    push_to_hub=False,
+    gradient_accumulation_steps=2
+)
+
+data_collator = DataCollatorForSeq2Seq(tokenizer, model=model)
+
+# computing metrics from the predictions
+def compute_metrics(eval_pred):
+    predictions, labels = eval_pred
+    decoded_preds = tokenizer.batch_decode(predictions, skip_special_tokens=True)
+    # Replace -100 in the labels as we can't decode them.
+    labels = np.where(labels != -100, labels, tokenizer.pad_token_id)
+    decoded_labels = tokenizer.batch_decode(labels, skip_special_tokens=True)
+    # Rouge expects a newline after each sentence
+    decoded_preds = ["\n".join(nltk.sent_tokenize(pred.strip())) for pred in decoded_preds]
+    decoded_labels = ["\n".join(nltk.sent_tokenize(label.strip())) for label in decoded_labels]
+    result = metric.compute(predictions=decoded_preds, references=decoded_labels, use_stemmer=True)
+    # Extract a few results
+    result = {key: value * 100 for key, value in result.items()}
+    # Add mean generated length
+    prediction_lens = [np.count_nonzero(pred != tokenizer.pad_token_id) for pred in predictions]
+    result["gen_len"] = np.mean(prediction_lens)
+    return {k: round(v, 4) for k, v in result.items()}
+
+
+# Define the training and evaluation datasets
+train_dataset = tokenized_dataset["train"]
+eval_dataset = tokenized_dataset["validation"]
+
+# Create the trainer object
+trainer = Seq2SeqTrainer(
+    model=model,
+    args=args,
+    train_dataset=train_dataset,
+    eval_dataset=eval_dataset,
+    data_collator=data_collator,
+    compute_metrics=compute_metrics,
+)
+
+# Train the model
+trainer.train()
+
+# Define the input and output interface of the app
+def summarizer(input_text):
+    inputs = [prefix + input_text]
+    model_inputs = tokenizer(inputs, max_length=max_input_length, truncation=True, return_tensors="pt")
+    summary_ids = model.generate(
+        input_ids=model_inputs["input_ids"],
+        attention_mask=model_inputs["attention_mask"],
+        num_beams=4,
+        length_penalty=2.0,
+        max_length=max_target_length + 2,  # +2 from original because we start at step=1 and stop before max_length
+        repetition_penalty=2.0,
+        early_stopping=True,
+        use_cache=True
+    )
+    summary = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
+    return summary
+
+# Interface creation and launching
+iface = gr.Interface(
+    fn=summarizer,
+    inputs=gr.inputs.Textbox(label="Input Text"),
+    outputs=gr.outputs.Textbox(label="Summary"),
+    title="Scientific Paper Summarizer",
+    description="Summarizes scientific papers using a fine-tuned T5 model",
+    theme="gray"
+)
+iface.launch()
+