Delete app.py

app.py

@@ -1,154 +0,0 @@
-import subprocess
-
-# Install the required packages
-subprocess.check_call(["pip", "install", "-U", "git+https://github.com/huggingface/transformers.git"])
-subprocess.check_call(["pip", "install", "-U", "git+https://github.com/huggingface/accelerate.git"])
-subprocess.check_call(["pip", "install", "datasets"])
-subprocess.check_call(["pip", "install", "evaluate"])
-subprocess.check_call(["pip", "install", "scikit-learn"])
-subprocess.check_call(["pip", "install", "torchvision"])
-
-model_checkpoint = "microsoft/resnet-50"
-batch_size = 128
-
-from datasets import load_dataset
-from evaluate import load
-
-metric = load("accuracy")
-
-# Load the dataset directly from Hugging Face
-dataset = load_dataset("DamarJati/Face-Mask-Detection")
-labels = dataset["train"].features["label"].names
-label2id, id2label = dict(), dict()
-for i, label in enumerate(labels):
-    label2id[label] = i
-    id2label[i] = label
-
-from transformers import AutoImageProcessor
-image_processor  = AutoImageProcessor.from_pretrained(model_checkpoint)
-image_processor
-
-from torchvision.transforms import (
-    CenterCrop,
-    Compose,
-    Normalize,
-    RandomHorizontalFlip,
-    RandomResizedCrop,
-    Resize,
-    ToTensor,
-    ColorJitter,
-    RandomRotation
-)
-
-normalize = Normalize(mean=image_processor.image_mean, std=image_processor.image_std)
-
-# Check if size is a dictionary with height and width keys
-if isinstance(image_processor.size, dict) and "height" in image_processor.size and "width" in image_processor.size:
-    size = (image_processor.size["height"], image_processor.size["width"])
-else:
-    size = (224, 224)  # Default size if not specified
-
-train_transforms = Compose(
-        [
-            RandomResizedCrop(size),
-            RandomHorizontalFlip(),
-            RandomRotation(degrees=15),
-            ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
-            ToTensor(),
-            normalize,
-        ]
-    )
-
-val_transforms = Compose(
-        [
-            Resize(size),
-            CenterCrop(size),
-            RandomRotation(degrees=15),
-            ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4, hue=0.1),
-            ToTensor(),
-            normalize,
-        ]
-    )
-
-def preprocess_train(example_batch):
-    example_batch["pixel_values"] = [
-        train_transforms(image.convert("RGB")) for image in example_batch["image"]
-    ]
-    return example_batch
-
-def preprocess_val(example_batch):
-    example_batch["pixel_values"] = [val_transforms(image.convert("RGB")) for image in example_batch["image"]]
-    return example_batch
-
-splits = dataset["train"].train_test_split(test_size=0.3)
-train_ds = splits['train']
-val_ds = splits['test']
-
-train_ds.set_transform(preprocess_train)
-val_ds.set_transform(preprocess_val)
-
-from transformers import AutoModelForImageClassification, TrainingArguments, Trainer
-
-model = AutoModelForImageClassification.from_pretrained(model_checkpoint,
-                                                        label2id=label2id,
-                                                        id2label=id2label,
-                                                        ignore_mismatched_sizes = True)
-
-model_name = model_checkpoint.split("/")[-1]
-
-args = TrainingArguments(
-    f"{model_name}-finetuned",
-    remove_unused_columns=False,
-    evaluation_strategy = "epoch",
-    save_strategy = "epoch",
-    save_total_limit = 5,
-    learning_rate=1e-3,
-    per_device_train_batch_size=batch_size,
-    gradient_accumulation_steps=2,
-    per_device_eval_batch_size=batch_size,
-    num_train_epochs=2,
-    warmup_ratio=0.1,
-    weight_decay=0.01,
-    lr_scheduler_type="cosine",
-    logging_steps=10,
-    load_best_model_at_end=True,
-    metric_for_best_model="accuracy",)
-
-import numpy as np
-
-def compute_metrics(eval_pred):
-    """Computes accuracy on a batch of predictions"""
-    predictions = np.argmax(eval_pred.predictions, axis=1)
-    return metric.compute(predictions=predictions, references=eval_pred.label_ids)
-
-import torch
-
-def collate_fn(examples):
-    pixel_values = torch.stack([example["pixel_values"] for example in examples])
-    labels = torch.tensor([example["label"] for example in examples])
-    return {"pixel_values": pixel_values, "labels": labels}
-
-trainer = Trainer(model,
-                  args,
-                  train_dataset=train_ds,
-                  eval_dataset=val_ds,
-                  tokenizer=image_processor,
-                  compute_metrics=compute_metrics,
-                  data_collator=collate_fn,)
-
-train_results = trainer.train()
-# Save model
-trainer.save_model()
-trainer.log_metrics("train", train_results.metrics)
-trainer.save_metrics("train", train_results.metrics)
-trainer.save_state()
-
-metrics = trainer.evaluate()
-# Log and save metrics
-trainer.log_metrics("eval", metrics)
-trainer.save_metrics("eval", metrics)
-
-# Print evaluation metrics
-print("Evaluation Metrics:")
-for key, value in metrics.items():
-    print(f"{key}: {value}")