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from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
# Authenticate and create the PyDrive client.
gauth = GoogleAuth()
gauth.LocalWebserverAuth()
drive = GoogleDrive(gauth)
# Replace 'file_id' with the actual ID of your file in Google Drive.
file_id = '1lDfR_B3fYM_rmC8H_HDtRdxJKveYWmOp'
# Create a GoogleDriveFile instance with the file ID.
file_obj = drive.CreateFile({'id': file_id})
# Download the file content.
downloaded_file_path = 'downloaded_model.pth'
file_obj.GetContentFile(downloaded_file_path)
print("ok")
# import gradio as gr
# from transformers import AutoModelForSequenceClassification, AutoTokenizer
# import torch
# from sentence_transformers import SentenceTransformer, models
# param_max_length=256
# # Define a function that takes a text input and returns the result
# def analyze_text(input):
# # Your processing or model inference code here
# result = predict_similarity(input)
# return result
# param_model_name="CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth"
# tokenizer = AutoTokenizer.from_pretrained(param_model_name)
# class BertForSTS(torch.nn.Module):
# def __init__(self):
# super(BertForSTS, self).__init__()
# #self.bert = models.Transformer('bert-base-uncased', max_seq_length=128)
# #self.bert = AutoModelForSequenceClassification.from_pretrained("CAMeL-Lab/bert-base-arabic-camelbert-msa-sixteenth")
# self.bert = models.Transformer(param_model_name, max_seq_length=param_max_length)
# dimension= self.bert.get_word_embedding_dimension()
# #print(dimension)
# self.pooling_layer = models.Pooling(dimension)
# self.dropout = torch.nn.Dropout(0.1)
# # relu activation function
# self.relu = torch.nn.ReLU()
# # dense layer 1
# self.fc1 = torch.nn.Linear(dimension,512)
# # dense layer 2 (Output layer)
# self.fc2 = torch.nn.Linear(512,512)
# #self.pooling_layer = models.Pooling(self.bert.config.hidden_size)
# self.sts_bert = SentenceTransformer(modules=[self.bert,self.pooling_layer, self.fc1])
# #self.sts_bert = SentenceTransformer(modules=[self.bert,self.pooling_layer, self.fc1, self.relu, self.dropout,self.fc2])
# def forward(self, input_data):
# #print(input_data)
# x=self.bert(input_data)
# x=self.pooling_layer(x)
# x=self.fc1(x['sentence_embedding'])
# x = self.relu(x)
# x = self.dropout(x)
# #x = self.fc2(x)
# return x
# model_load_path = "model.pt"
# model = BertForSTS()
# model.load_state_dict(torch.load(model_load_path))
# model.to(device)
# def predict_similarity(sentence_pair):
# test_input = tokenizer(sentence_pair, padding='max_length', max_length = param_max_length, truncation=True, return_tensors="pt").to(device)
# test_input['input_ids'] = test_input['input_ids']
# print(test_input['input_ids'])
# test_input['attention_mask'] = test_input['attention_mask']
# del test_input['token_type_ids']
# output = model(test_input)
# sim = torch.nn.functional.cosine_similarity(output[0], output[1], dim=0).item()*2-1
# return sim
# # Create a Gradio interface with a text input zone
# iface = gr.Interface(
# fn=analyze_text, # The function to be called with user input
# inputs=[gr.Textbox(), gr.Textbox()],
# outputs="text" # Display the result as text
# )
# # # Launch the Gradio interface
# iface.launch() |