from langchain.chains import RetrievalQA
from langchain.llms import HuggingFacePipeline
from transformers import pipeline
from modules import parser, vectorizer

def run_analysis(uploaded_files, text_input, query, quick_action, temperature, start_time, end_time):
    """
    Main logic that runs when the user clicks 'Analyze Logs'.
    It combines file and text inputs, applies embeddings,
    and performs question answering using a language model.
    """
    logs_text = ""

    # Combine all uploaded files into one text string
    if uploaded_files:
        logs_text += parser.parse_uploaded_files(uploaded_files)

    # Add manual pasted text logs
    if text_input:
        logs_text += "\n" + text_input

    # Show error if no log input provided
    if not logs_text.strip():
        return "❌ No logs provided.", None, None, None

    # Use either free-form query or a quick action
    query_text = query if query else quick_action
    if not query_text:
        return "❌ No query provided.", None, None, None

    # Chunk logs and embed them
    docs = vectorizer.prepare_documents(logs_text)
    vectordb = vectorizer.create_vectorstore(docs)

    # Load a small Hugging Face text generation pipeline (GPT-2 here)
    pipe = pipeline("text-generation", model="gpt2", max_length=512, temperature=temperature)
    llm = HuggingFacePipeline(pipeline=pipe)

    # Create LangChain retrieval-based QA chain
    qa = RetrievalQA.from_chain_type(llm=llm, retriever=vectordb.as_retriever())

    # Run the query against embedded document chunks
    result = qa.run(query_text)

    # -------- Mocked example chart and alert outputs --------
    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 structured outputs to Gradio UI
    return result, bar_data, pie_data, alerts