streamlit_app.py

import streamlit as st
import os
import hashlib
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
import torch
from upload_to_s3 import upload_file_to_s3
from notify_slack import send_slack_alert

# Load model
model_path = "Tuathe/codementor-flan-watchtower"  
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForSeq2SeqLM.from_pretrained(model_path)

# Secrets from Hugging Face Spaces
aws_key = st.secrets["AWS_ACCESS_KEY_ID"]
aws_secret = st.secrets["AWS_SECRET_ACCESS_KEY"]
aws_region = st.secrets["AWS_REGION"]
bucket_name = st.secrets["S3_BUCKET_NAME"]
slack_url = st.secrets["SLACK_WEBHOOK_URL"]

# Fallback classifier
def fallback_label(log):
    log_lower = log.lower()
    if "login" in log_lower and "failed" in log_lower:
        return "SECURITY"
    elif "error" in log_lower or "failed" in log_lower:
        return "ERROR"
    elif "timeout" in log_lower or "not responding" in log_lower:
        return "CRITICAL"
    elif "cpu" in log_lower or "memory" in log_lower:
        return "WARNING"
    else:
        return "INFO"

# Classifier
def classify_log(log):
    prompt = f"""Classify this log message as one of: INFO, WARNING, ERROR, CRITICAL, SECURITY.

Log: {log}

Label:"""
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=3)
    prediction = tokenizer.decode(outputs[0], skip_special_tokens=True).strip().upper()
    valid_labels = {"INFO", "WARNING", "ERROR", "CRITICAL", "SECURITY"}
    return prediction if prediction in valid_labels else fallback_label(log), ("Model" if prediction in valid_labels else "Fallback")

# Runbook generator
def generate_runbook(log_text, label):
    prompt = f"""
You are an expert SRE. Create a step-by-step runbook in markdown format for the following {label} log.

Log message: "{log_text}"

Include:
1. Summary
2. Possible causes
3. Troubleshooting steps
4. Mitigation actions
5. Responsible team or escalation

Only output valid markdown text.
"""
    inputs = tokenizer(prompt, return_tensors="pt", truncation=True)
    with torch.no_grad():
        outputs = model.generate(**inputs, max_new_tokens=300)
    runbook_text = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()

    hash_id = hashlib.md5(log_text.encode()).hexdigest()[:8]
    runbook_path = f"runbooks/runbook_{hash_id}.md"

    os.makedirs("runbooks", exist_ok=True)
    with open(runbook_path, "w", encoding="utf-8") as f:
        f.write(runbook_text)

    return runbook_path, runbook_text

# Streamlit UI
st.set_page_config(page_title="WatchTowerAI", layout="centered")
st.title("  WatchTowerAI - Log Classification + Runbook Generator")

log_input = st.text_input(" Enter a log message")
uploaded_file = st.file_uploader(" Or upload a .log file", type=["txt", "log"])

if st.button(" Classify + Generate Runbook"):
    logs = []
    if log_input:
        logs.append(log_input.strip())
    if uploaded_file:
        content = uploaded_file.read().decode("utf-8")
        logs.extend([line.strip() for line in content.splitlines() if line.strip()])

    if not logs:
        st.warning("Please enter a log or upload a file.")
    else:
        for log in logs:
            with st.spinner(f"Processing: {log}"):
                label, source = classify_log(log)
                st.markdown(f"**Classification:** `{label}` via `{source}`")
                st.markdown(f"**Log:** {log}")

                if label in {"CRITICAL", "SECURITY"}:
                    runbook_path, runbook_md = generate_runbook(log, label)

                    s3_path = runbook_path.replace("\\", "/")
                    success = upload_file_to_s3(runbook_path, bucket_name, s3_path, aws_key, aws_secret, aws_region)

                    if success:
                        s3_url = f"https://{bucket_name}.s3.{aws_region}.amazonaws.com/{s3_path}"
                        send_slack_alert(log, s3_url)
                        st.success(" Slack alert sent.")
                        st.markdown(f"[ View Runbook on S3]({s3_url})")
                        st.download_button(" Download Runbook", runbook_md, file_name=os.path.basename(runbook_path))
                    else:
                        st.error(" Failed to upload runbook to S3.")
                else:
                    st.info(" No runbook generated for this log.")