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e1732a364fed commited on
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3a60eea
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1 Parent(s): 26b720e

basic feature

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Files changed (2) hide show
  1. .gitignore +3 -0
  2. app.py +116 -3
.gitignore ADDED
@@ -0,0 +1,3 @@
 
 
 
 
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+ __pycache__/
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+ .venv/
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+ flagged/
app.py CHANGED
@@ -1,9 +1,122 @@
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  import gradio as gr
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- def greet(name):
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- return "Hello " + name + "!!"
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- demo = gr.Interface(fn=greet, inputs="text", outputs="text")
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  demo.launch()
 
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+ def extract_body_text_by_string(input_string, max_len=512):
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+ string_length = len(input_string)
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+
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+ if string_length <= max_len:
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+ return input_string.strip()
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+
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+ chunk_size = max_len // 3 # 三等分
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+ positions = [0, string_length // 2, string_length - chunk_size] # 头、中、尾
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+
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+ extracted_text = []
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+
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+ for pos in positions:
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+ text_chunk = input_string[pos : pos + chunk_size]
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+ extracted_text.append(text_chunk.strip())
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+
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+ return "".join(extracted_text)
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+
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+
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+ import torch
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+
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+ DEVICE = torch.device(
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+ "cuda"
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+ if torch.cuda.is_available()
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+ else "mps" if torch.backends.mps.is_available() else "cpu"
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+ )
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+
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+ import numpy as np
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+
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+
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+ def predict_text(text, model, tokenizer, max_len, device=DEVICE):
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+ encoding = tokenizer.encode_plus(
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+ text,
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+ add_special_tokens=True,
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+ max_length=max_len,
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+ padding="max_length",
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+ truncation=True,
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+ return_attention_mask=True,
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+ return_tensors="pt",
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+ )
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+
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+ input_ids = encoding["input_ids"].to(device)
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+ attention_mask = encoding["attention_mask"].to(device)
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+
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+ with torch.no_grad():
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+ outputs = model(input_ids, attention_mask=attention_mask)
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+ # print("outputs", outputs)
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+ logits = outputs.logits.cpu().numpy()
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+ pred = np.argmax(logits, axis=1)[0]
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+
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+ return "ok" if pred == 0 else "ban"
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+
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+
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  import gradio as gr
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+ print("loading models...")
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+ from transformers import (
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+ BertTokenizer,
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+ BertForSequenceClassification,
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+ )
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+
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+ head_tokenizer = BertTokenizer.from_pretrained(
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+ f"e1732a364fed/bert-geosite-classification-head-v1"
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+ )
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+ body_tokenizer = BertTokenizer.from_pretrained(
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+ f"e1732a364fed/bert-geosite-classification-body-v1"
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+ )
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+ head_model = BertForSequenceClassification.from_pretrained(
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+ f"e1732a364fed/bert-geosite-classification-head-v1"
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+ ).to(DEVICE)
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+ body_model = BertForSequenceClassification.from_pretrained(
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+ f"e1732a364fed/bert-geosite-classification-body-v1"
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+ ).to(DEVICE)
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+
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+ head_model.eval()
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+ body_model.eval()
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+
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+ print("loaded models...")
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+
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+
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+ def func(head, body):
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+ print("predicting head")
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+
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+ h_result = predict_text(head, head_model, head_tokenizer, 512)
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+ print("predicting body")
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+
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+ b_result = predict_text(body, body_model, body_tokenizer, 512)
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+ print("prediction done")
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+
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+ return h_result, b_result
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+ demo = gr.Interface(
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+ fn=func,
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+ inputs=[
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+ gr.Textbox(
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+ label="Head",
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+ info="http response head",
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+ lines=6,
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+ max_lines=10000000000,
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+ value="200 OK",
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+ ),
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+ gr.Textbox(
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+ label="Body",
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+ info="http response body",
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+ lines=6,
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+ max_lines=10000000000,
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+ value="<body>The quick brown fox jumped over the lazy dogs.</body>",
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+ ),
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+ ],
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+ outputs=[
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+ gr.Textbox(
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+ label="Head",
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+ info="http response prediction",
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+ ),
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+ gr.Textbox(
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+ label="Body",
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+ info="http response prediction",
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+ ),
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+ ],
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+ )
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  demo.launch()