Update app.py

app.py

@@ -9,9 +9,92 @@ import evaluate
 import numpy as np
 import random
 
-def process(model, dataset):
+def preprocess_function(examples):
+    return tokenizer(examples["text"], padding="max_length", truncation=True)
+        
+def process(model_id, dataset):
+    # Step 1: Load dataset
     dataset_imdb = load_dataset(dataset)
+
+    # Step 2: Reduce dataset (optional)
+
+    reduction_rate = 0.1
+    num_train_to_keep = int(reduction_rate * dataset_imdb["train"].num_rows)
+    num_test_to_keep = int(reduction_rate * dataset_imdb["test"].num_rows)
+    
+    def select_random_indices(dataset, num_to_keep):
+        indices = list(range(dataset.num_rows))
+        random.shuffle(indices)
+        return indices[:num_to_keep]
+    
+    train_indices = select_random_indices(dataset_imdb["train"], num_train_to_keep)
+    test_indices = select_random_indices(dataset_imdb["test"], num_test_to_keep)
+    
+    dataset_imdb = DatasetDict({
+        "train": dataset_imdb["train"].select(train_indices),
+        "test": dataset_imdb["test"].select(test_indices),
+    })
+
+    # Step 3: Text tokenization
+
+    tokenizer = AutoTokenizer.from_pretrained("bert-base-cased")
+
+    # Step 4: Apply tokenization to dataset
+
+    tokenized_imdb = dataset_imdb.map(preprocess_function, batched=True)
+
+    #Step 5: Fine-tune the model
+
+    model_id = model_id
+    model = AutoModelForSequenceClassification.from_pretrained(model_id)
+    
+    lora_config = LoraConfig(task="sequence_classification")
+    peft_model = PeftModel(model, lora_config)
+    
+    training_args = TrainingArguments(
+        output_dir="./results",
+        num_train_epochs=3,
+        per_device_train_batch_size=16,
+        per_device_eval_batch_size=64,
+        evaluation_strategy="epoch",
+        learning_rate=1e-5,
+        save_total_limit=2,
+        save_steps=500,
+        load_best_model_at_end=True,
+        metric_for_best_model="accuracy",
+        greater_is_better=True,
+        save_strategy="steps",
+        eval_accumulation_steps=10,
+    )
+    
+    trainer = Trainer(
+        model=peft_model,
+        args=training_args,
+        train_dataset=tokenized_imdb["train"],
+        eval_dataset=tokenized_imdb["test"],
+        compute_metrics=lambda pred: {"accuracy": torch.sum(pred.label_ids == pred.predictions.argmax(-1)).item()},
+        data_collator=DataCollatorWithPadding(tokenizer=tokenizer),
+    )
+    
+    trainer.train()
+
+    # Step 6: Evaluate the fine-tuned model
+
+    targets = []
+    predictions = []
+    for i in range(len(tokenized_imdb["test"])):
+        review = tokenized_imdb["test"][i]["text"]
+        target_sentiment = tokenized_imdb["test"][i]["label"]
+        predicted_sentiment = predict_sentiment(review)
+        if predicted_sentiment in ["positive", "negative"]:
+            targets.append(target_sentiment)
+            predictions.append(predicted_sentiment)
+        print(f"Record {i+1} - Actual: {target_sentiment}, Predicted: {predicted_sentiment}")
+    
     return "Done"
 
-demo = gr.Interface(fn=process, inputs=["model", "dataset"], outputs="text")
+demo = gr.Interface(fn=process, 
+                    inputs=[gr.Textbox(label = "Model ID", value = "google/gemma-7b", lines = 1),
+                            gr.Textbox(label = "Dataset", value = "imdb", lines = 1)],
+                    outputs=[gr.Textbox(label = "Completion")])
 demo.launch()