app.py

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 + "!!"

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()