Create app.py

app.py

@@ -0,0 +1,73 @@
+import gradio as gr
+import json
+from transformers import AutoModelForSequenceClassification, Trainer, TrainingArguments, AutoTokenizer
+from datasets import Dataset
+
+# Load preprocessed and tokenized data
+def load_data(preprocessed_file, tokenized_file):
+    with open(preprocessed_file.name, 'r') as f:
+        preprocessed_data = json.load(f)
+    
+    with open(tokenized_file.name, 'r') as f:
+        tokenized_data = json.load(f)
+    
+    return preprocessed_data, tokenized_data
+
+# Fine-tune the model
+def fine_tune_model(preprocessed_file, tokenized_file, progress=gr.Progress()):
+    preprocessed_data, tokenized_data = load_data(preprocessed_file, tokenized_file)
+    
+    # Convert tokenized data to Dataset
+    dataset = Dataset.from_dict(tokenized_data)
+    
+    # Split the dataset into train and validation sets
+    tokenized_datasets = dataset.train_test_split(test_size=0.2)
+    
+    model = AutoModelForSequenceClassification.from_pretrained('anferico/bert-for-patents', num_labels=2)
+    tokenizer = AutoTokenizer.from_pretrained('anferico/bert-for-patents')
+    
+    training_args = TrainingArguments(
+        output_dir='./results',
+        num_train_epochs=3,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=64,
+        warmup_steps=500,
+        weight_decay=0.01,
+        logging_dir='./logs',
+        logging_steps=10,
+        evaluation_strategy="epoch",
+        save_strategy="epoch",
+        load_best_model_at_end=True,
+    )
+    
+    trainer = Trainer(
+        model=model,
+        args=training_args,
+        train_dataset=tokenized_datasets['train'],
+        eval_dataset=tokenized_datasets['test'],
+    )
+    
+    progress(0.5, "Fine-tuning the model...")
+    trainer.train()
+    progress(1.0, "Fine-tuning complete.")
+    
+    model.save_pretrained('./fine_tuned_patentbert')
+    tokenizer.save_pretrained('./fine_tuned_patentbert')
+    
+    return "Model fine-tuned and saved successfully."
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=fine_tune_model,
+    inputs=[
+        gr.File(label="Upload Preprocessed Data JSON"),
+        gr.File(label="Upload Tokenized Data JSON")
+    ],
+    outputs=gr.Textbox(label="Processing Information"),
+    title="Fine-Tune Patent BERT Model",
+    description="Upload preprocessed and tokenized JSON files to fine-tune the BERT model.",
+    live=True  # Enable live updates for progress
+)
+
+# Launch the interface
+iface.launch()