Delete inference

inference/analyze.py

@@ -1,59 +0,0 @@
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from rag.search import search_context
-import os
-
-BASE_DIR = os.path.dirname(os.path.dirname(__file__))
-
-MODEL_PATH = os.path.join(BASE_DIR, "model", "final")
-
-tokenizer = AutoTokenizer.from_pretrained(
-    MODEL_PATH,
-    local_files_only=True,
-    trust_remote_code=True,
-    fix_mistral_regex=True
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_PATH,
-    local_files_only=True,
-    trust_remote_code=True,
-    device_map="auto",
-    dtype=torch.float16
-)
-
-
-def analyze(user_input: str):
-    context = search_context(user_input)
-
-    prompt = f"""
-You are a cybersecurity malware analysis assistant.
-Respond ONLY in valid JSON.
-Use these fields exactly once:
-- reasoning (array of strings)
-- indicators (array)
-- confidence (float 0-1)
-- recommendation (string)
-- mitre_attack (array)
-
-Context:
-{context}
-
-Input:
-{user_input}
-
-Response:
-"""
-
-    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-    with torch.no_grad():
-        output = model.generate(
-            **inputs,
-            max_new_tokens=256,
-            do_sample=True,
-            temperature=0.2,
-            top_p=0.9
-        )
-
-    return tokenizer.decode(output[0], skip_special_tokens=True)

inference/test.py

@@ -1,51 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-import torch
-import os
-
-MODEL_PATH = os.path.abspath(
-    r"C:\Users\USER\OneDrive\Desktop\work\mcma\micro-cyber-llm\model\final"
-)
-
-tokenizer = AutoTokenizer.from_pretrained(
-    MODEL_PATH,
-    local_files_only=True,
-    fix_mistral_regex=True
-)
-
-model = AutoModelForCausalLM.from_pretrained(
-    MODEL_PATH,
-    device_map="auto",
-    dtype=torch.float16,
-    local_files_only=True
-)
-
-prompt = """### Instruction:
-You are a cybersecurity malware analysis assistant.
-Respond ONLY in valid JSON.
-Use these fields exactly once:
-- reasoning (array of strings)
-- indicators (array)
-- confidence (float 0-1)
-- recommendation (string)
-- mitre_attack (array)
-
-
-### Input:
-APK requests READ_SMS and communicates with api.telegram.org
-
-### Response:
-"""
-
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-
-with torch.no_grad():
-    output = model.generate(
-    **inputs,
-    max_new_tokens=256,
-    do_sample=True,
-    temperature=0.2,
-    top_p=0.9
-)
-
-
-print(tokenizer.decode(output[0], skip_special_tokens=True))