Update app.py

app.py

@@ -1,87 +1,99 @@
-import gradio as gr
-from transformers import AutoModelForSequenceClassification, AutoTokenizer
-import torch
-from sentence_transformers import SentenceTransformer, models
-param_max_length=256
+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
-
-# import requests
-
-# file_url = "https://drive.google.com/uc?export=download&id=1lDfR_B3fYM_rmC8H_HDtRdxJKveYWmOp"
-# response = requests.get(file_url)
-
-# with open("model.pt", "wb") as f:
-#     f.write(response.content)
-#     print(response.content)
-# f.close()
-
-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()
+# # 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()