Update app.py

app.py

@@ -1,5 +1,58 @@
 import gradio as gr
 
+# Initialize your model: Use the Hugging Face library to initialize your model with the chosen pre-trained model architecture
+from transformers import BertForSequenceClassification
+model = BertForSequenceClassification.from_pretrained("bert-base-uncased", num_labels=2)
+
+#Tokenize your data: Tokenize your input data using the tokenizer provided by Hugging Face for the specific model you're using. 
+#This step converts text inputs into numerical representations that the model can process.
+from transformers import BertTokenizer
+tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
+
+#Tokenize the input text
+text = "Hello, how are you?"
+tokens = tokenizer.encode(text, add_special_tokens=True)
+
+#Convert tokens to input IDs
+input_ids = tokenizer.convert_tokens_to_ids(tokens)
+
+#Attention masks
+attention_mask = tokenizer.create_attention_mask(input_ids)
+
+#Create data loaders: Create data loaders or data iterators to efficiently load and batch your tokenized data during training. 
+#Hugging Face provides tools like DataLoader or DataProcessor for this purpose.
+from transformers import DataLoader
+
+#Prepare your tokenized data and Create a dataset
+from torch.utils.data import TensorDataset
+dataset = TensorDataset(input_ids, attention_mask, labels)
+
+#Create a data loader
+batch_size = 32
+shuffle = True
+data_loader = DataLoader(dataset, batch_size=batch_size, shuffle=shuffle)
+
+#Iterate through the data loader and perform training step using the batched data
+for batch in data_loader:
+    input_ids_batch, attention_mask_batch, labels_batch = batch
+
+#Define your training loop: Write the training loop using PyTorch or TensorFlow, depending on the framework supported by the Hugging Face model you are using. 
+#Within the loop, you'll need to define the loss function, optimizer, and any additional metrics you want to track.
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+learning_rate = 0.001
+optimizer = optim.Adam(model.parameters(), lr=learning_rate)
+
+#Fine-tune the model: Train the model on your dataset using the training loop. 
+#Adjust the hyperparameters such as learning rate, batch size, and number of epochs to optimize performance. 
+#Monitor the validation set metrics to avoid overfitting and select the best model based on these metrics.
+
+
+#Evaluate the model: Once training is complete, evaluate the performance of your trained model on the test set. Calculate relevant metrics such as accuracy, precision, recall, or F1 score.
+#Save and load the model: Save the trained model parameters to disk so that you can later load and use it for predictions without having to retrain from scratch.
+
 def greet(name):
     return "Hello " + name + "!!"