rename app.py

main.py → app.py

@@ -1,63 +1,63 @@
-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__":
+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()