add dataset output

tools/generate.py

@@ -0,0 +1,60 @@
+import openai
+import os
+import json
+import glob
+import time
+openai.api_key = os.getenv("OPENAI_API_KEY")
+
+
+def get_bpf_summary(bpf_code):
+    response = openai.ChatCompletion.create(
+        model="gpt-3.5-turbo",
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant."},
+            {"role": "user", "content": 
+                f"""
+Given this BPF code:
+
+{bpf_code}
+
+Provide a concise and clear summary in one or two sentences. 
+Frame the explanation as a user's request to a developer to write a BPF code. 
+Avoid beginning with phrases like 'This BPF code is...' Instead, 
+start with action-oriented phrases such as 'Write a BPF code that...'
+"""
+
+
+                }
+        ],
+        max_tokens=1500
+    )
+
+    output = response.choices[0].message['content']
+    return output
+
+
+def get_all_bpf_in_dir(directory):
+    all_files = glob.glob(directory + '/*.bt')
+    all_bpf = []
+    for file in all_files:
+        print("opening file: " + file)
+        # sleep for a short time to avoid hitting the rate limit
+        # Sleep for 5 seconds
+        time.sleep(3)
+        with open(file, 'r') as f:
+            bpf_code = f.read()
+            summary = get_bpf_summary(bpf_code)
+            all_bpf.append({
+                "request": summary,
+                "bpf": bpf_code
+            })
+            print(bpf_code)
+            print(summary)
+
+    return all_bpf
+
+
+directory = './'
+all_bpf = get_all_bpf_in_dir(directory)
+with open('output.json', 'w') as outfile:
+    json.dump(all_bpf, outfile)