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from langchain.chains import RetrievalQA
from langchain_community.llms import HuggingFacePipeline # ✅ Updated import
from transformers import pipeline
from modules import parser, vectorizer
def run_analysis(uploaded_files, text_input, query, quick_action, temperature, start_time, end_time):
logs_text = ""
if uploaded_files:
logs_text += parser.parse_uploaded_files(uploaded_files)
if text_input:
logs_text += "\n" + text_input
if not logs_text.strip():
return "❌ No logs provided.", None, None, None
query_text = query if query else quick_action
if not query_text:
return "❌ No query provided.", None, None, None
docs = vectorizer.prepare_documents(logs_text)
vectordb = vectorizer.create_vectorstore(docs)
pipe = pipeline("text-generation", model="gpt2", max_length=512, temperature=temperature)
llm = HuggingFacePipeline(pipeline=pipe)
qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectordb.as_retriever())
result = qa.run(query_text)
# Example mock output
bar_data = {"Hour": ["14:00", "15:00"], "Count": [8, 4]}
pie_data = {"Event Type": ["Blocked", "Scan"], "Count": [8, 4]}
alerts = [("CRITICAL", "8 blocked SSH attempts from 192.168.1.5"),
("WARNING", "4 port scanning alerts from 10.0.0.8")]
return result, bar_data, pie_data, alerts
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