import gradio as gr
import spacy
import math
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModel
import torch
import torch.nn.functional as F


#Mean Pooling - Take attention mask into account for correct averaging
# def mean_pooling(model_output, attention_mask):
#     token_embeddings = model_output[0] #First element of model_output contains all token embeddings
#     input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
#     return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)


# def training():
#     dataset = load_dataset("glue", "cola")
#     dataset = dataset["train"]

#     sentences = ["This is an example sentence", "Each sentence is converted"]

#     model = SentenceTransformer('sentence-transformers/all-MiniLM-L6-v2')
#     embeddings = model.encode(sentences)
#     print(embeddings)
    
#     # Sentences we want sentence embeddings for
#     sentences = ['This is an example sentence', 'Each sentence is converted']

#     # Load model from HuggingFace Hub
#     tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
#     model = AutoModel.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')

#     # Tokenize sentences
#     encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')

#     # Compute token embeddings
#     with torch.no_grad():
#         model_output = model(**encoded_input)

#     # Perform pooling
#     sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])

#     # Normalize embeddings
#     sentence_embeddings = F.normalize(sentence_embeddings, p=2, dim=1)

#     print("Sentence embeddings:")
#     print(sentence_embeddings)
    

def greet(name):
    return "Hello " + name + "!!"


# def main():
#     return 0

iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch()
    
# if __name__ == "__main__":
#     main()