Update modules/analysis.py

modules/analysis.py

@@ -6,39 +6,3 @@ from modules import parser, vectorizer
 def run_analysis(uploaded_files, text_input, query, quick_action, temperature, start_time, end_time):
     print("🔥 run_analysis called")  # Will appear in Application Logs
     return "Function was triggered!", None, None, [("INFO", "Test log processed")]
-
-
-"""def run_analysis(uploaded_files, text_input, query, quick_action, temperature, start_time, end_time):
-    print("🔥 run_analysis called")
-    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""
-