AquilaX-AI
/

NL-JSON-248M

@@ -2,198 +2,79 @@
 library_name: transformers
 tags: []
 ---
-# Model Card for Model ID
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-## Model Details
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 library_name: transformers
 tags: []
 ---
+### Requirements
+```bash
+pip install transformers[torch]
+```
+### Inference
+```python
+import json
+import requests
+def convert_to_json(answer):
+    # Replace '<pad>' and '</s>' as per the provided logic
+    answer = answer.replace("<pad>", "").replace("</s>", "")
+    answer = answer.strip("'")  # Remove leading and trailing single quotes
+    # Replace 'false' with 'False' and 'true' with 'True' for Python compatibility
+    answer = answer.replace("false", "False").replace("true", "True")
+    # Convert the string to a dictionary
+    answer_dict = eval(answer)
+    # Convert the dictionary to a JSON object
+    answer_json = json.dumps(answer_dict)
+    # Load the JSON object to ensure it's correctly formatted
+    json_data = json.loads(answer_json)
+    return json_data
+def valid_url(url):
+    valid_list = [
+        "github.com", "bitbucket.org", "sourceforge.net", "aws.amazon.com",
+        "dev.azure.com", "gitea.com", "gogs.io", "phabricator.com",
+        "gitkraken.com", "beanstalkapp.com", "gitlab.com"
+    ]
+    platform = url.split("//")[1].split("/")[0]
+    if platform in valid_list:
+        return True
+    return {'message': 'Provide a valid URL for scanning. Currently, we support PII_Scanner, SAST_Scanner, Sac_Scanner (Open_Source_Security), IaC_Scanner, Container_Scanner'}
+```
+```python
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+import torch
+import time
+tokenizer = AutoTokenizer.from_pretrained("AquilaX-AI/NL-JSON-77M")
+model = AutoModelForSeq2SeqLM.from_pretrained("AquilaX-AI/NL-JSON-77M")
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+# Change YOUR_QUERY eg: can this https://github.com/mr-vicky-01/educational-assitant on every week using pii and sast scan
+query = "convert my query into json: " + "YOUR_QUERY".lower()
+start = time.time()
+inputs = tokenizer(query, return_tensors="pt")
+model.to(device)
+inputs = inputs.to(device)
+outputs = model.generate(**inputs, max_length=256)
+answer = tokenizer.decode(outputs[0])
+json_data = convert_to_json(answer)
+try:
+    valid = valid_url(json_data["repo"])
+    if valid == True:
+        print(json_data)
+    else:
+        print(valid)
+except:
+    print(json_data)
+end = time.time()
+print(f"Time taken: {end - start}")
+```