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#!/usr/bin/env python3
"""
Llama 3 CLI-Agent Server
────────────────────────
Gemini-CLI style planner + executor:
• plan with Meta-Llama-3-8B-Instruct
• steps: shell, read_file, write_file, edit_file, append_file, list_dir, python, respond
• robust JSON extraction (balanced braces) to avoid parse failures
"""
from flask import Flask, request, jsonify, Response, send_from_directory
from huggingface_hub import snapshot_download
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
import subprocess, os, json, traceback, io, contextlib
from pathlib import Path
import re
import os
import time
import sqlite3
from datetime import datetime
from functools import wraps
from flask import g
import platform
import shutil
import shlex
import torch
from string import Template
API_DB_PATH = os.environ.get("API_DB_PATH", "./api_keys.sqlite3")
MODEL_ID = os.environ.get("MODEL_ID", "TinyLlama/TinyLlama-1.1B-Chat-v1.0")
# ──────────────────────────────────────────────
# 3) Flask app & actions executor
# ──────────────────────────────────────────────
app = Flask(__name__, static_folder="public", static_url_path="")
SERVER_OS = platform.system().lower()
ALLOW_AUTO_INSTALL = os.environ.get("ALLOW_AUTO_INSTALL", "0") == "1"
MODEL_NAME = "TinyLlama-1.1B-Chat-v1.0"
def get_db():
if "db" not in g:
g.db = sqlite3.connect(API_DB_PATH, check_same_thread=False)
g.db.row_factory = sqlite3.Row
return g.db
@app.teardown_appcontext
def close_db(exc):
db = g.pop("db", None)
if db:
db.close()
def init_db():
db = get_db()
db.execute("""
CREATE TABLE IF NOT EXISTS api_keys(
id INTEGER PRIMARY KEY AUTOINCREMENT,
api_key TEXT UNIQUE, -- raw key stored directly
label TEXT,
created_at TEXT NOT NULL,
last_used TEXT,
active INTEGER NOT NULL DEFAULT 1
)
""")
db.commit()
def _bearer_or_header_key() -> str | None:
auth = request.headers.get("Authorization", "")
if auth.startswith("Bearer "):
return auth.split(" ", 1)[1].strip()
xk = request.headers.get("X-API-Key")
return xk.strip() if xk else None
def validate_api_key() -> dict | None:
key = _bearer_or_header_key()
if not key:
return None
db = get_db()
row = db.execute(
"SELECT id, active FROM api_keys WHERE api_key=?",
(key,)
).fetchone()
if not row or row["active"] != 1:
return None
# Update last_used
db.execute(
"UPDATE api_keys SET last_used=? WHERE id=?",
(datetime.utcnow().isoformat(timespec='seconds'), row["id"])
)
db.commit()
return dict(row)
def require_api_key(fn):
@wraps(fn)
def _wrap(*args, **kwargs):
ok = validate_api_key()
if not ok:
return jsonify({"error": "Unauthorized"}), 401
return fn(*args, **kwargs)
return _wrap
# ──────────────────────────────────────────────
# 0) Helpers
# ──────────────────────────────────────────────
def extract_first_json_object(text: str) -> dict:
"""
Return the first valid top-level JSON object in `text` by scanning for balanced braces.
Raises ValueError if none found.
"""
start = text.find("{")
if start < 0:
raise ValueError("no '{' found")
depth = 0
in_string = False
escape = False
for i in range(start, len(text)):
ch = text[i]
if in_string:
if escape:
escape = False
elif ch == "\\":
escape = True
elif ch == '"':
in_string = False
else:
if ch == '"':
in_string = True
elif ch == "{":
depth += 1
elif ch == "}":
depth -= 1
if depth == 0:
candidate = text[start : i + 1]
return json.loads(candidate)
raise ValueError("no balanced JSON object found")
def safe_exec_python(code):
"""Run arbitrary python code in isolation and capture stdout/stderr tracebacks."""
buf = io.StringIO()
with contextlib.redirect_stdout(buf):
try:
exec(code, {"__name__": "__main__"})
except Exception:
traceback.print_exc()
return buf.getvalue()
# ──────────────────────────────────────────────
# 1) Model Loader — robust, self-healing
# ──────────────────────────────────────────────
def load_llm(model_id: str = MODEL_ID):
local_dir = Path("./tinyllama_1_1b_chat").resolve()
def have_min_tok(p: Path) -> bool:
return (p / "tokenizer.json").exists() or (p / "tokenizer.model").exists()
if not local_dir.exists() or not have_min_tok(local_dir):
print(f"[+] Downloading {model_id} into {local_dir} …")
snapshot_download(
repo_id=model_id,
local_dir=str(local_dir),
local_dir_use_symlinks=False,
revision="main",
)
print(f"[+] Loading TinyLlama from {local_dir} (CPU)")
tokenizer = AutoTokenizer.from_pretrained(
str(local_dir),
use_fast=True,
local_files_only=True,
trust_remote_code=True,
)
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
model = AutoModelForCausalLM.from_pretrained(
str(local_dir),
device_map="cpu", # ← force CPU
torch_dtype=torch.float32, # ← CPU-friendly dtype
low_cpu_mem_usage=True,
local_files_only=True,
trust_remote_code=True,
)
pipe = pipeline(
task="text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=256, # keep small for free CPU
do_sample=False,
return_full_text=False,
pad_token_id=tokenizer.pad_token_id,
eos_token_id=tokenizer.eos_token_id,
)
return pipe
def llm_chat(pipe, system_prompt: str, user_prompt: str) -> str:
tok = pipe.tokenizer
mdl = pipe.model
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
]
# build chat prompt with special tokens
input_ids = tok.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to(mdl.device)
outputs = mdl.generate(
input_ids=input_ids,
max_new_tokens=512,
do_sample=False,
eos_token_id=tok.eos_token_id,
pad_token_id=tok.pad_token_id,
)
# Only the generated continuation
gen_ids = outputs[0][input_ids.shape[-1]:]
text = tok.decode(gen_ids, skip_special_tokens=True)
return text
def llm_generate_text(pipe, system_prompt: str, user_prompt: str, max_new_tokens: int = 1200) -> str:
tok = pipe.tokenizer
mdl = pipe.model
messages = [
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt},
]
input_ids = tok.apply_chat_template(
messages,
tokenize=True,
add_generation_prompt=True,
return_tensors="pt",
).to(mdl.device)
outputs = mdl.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=True, # allow creativity for content
temperature=0.7,
top_p=0.95,
eos_token_id=tok.eos_token_id,
pad_token_id=tok.pad_token_id,
)
gen_ids = outputs[0][input_ids.shape[-1]:]
return tok.decode(gen_ids, skip_special_tokens=True)
# --- Actionability helpers ---
# ACTIONABLE set
ACTIONABLE = {
"shell","read_file","write_file","edit_file","append_file",
"list_dir","python","generate_file","mkdirs","generate_tree","generate_large_file",
"rewrite_file","fs" # ← new
}
def llm_generate_text_exact(pipe, system_prompt: str, user_prompt: str, max_new_tokens: int = 1200) -> str:
tok = pipe.tokenizer
mdl = pipe.model
messages = [{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}]
input_ids = tok.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt").to(mdl.device)
outputs = mdl.generate(
input_ids=input_ids,
max_new_tokens=max_new_tokens,
do_sample=False, # ← deterministic
temperature=0.0,
top_p=1.0,
eos_token_id=tok.eos_token_id,
pad_token_id=tok.pad_token_id,
)
gen_ids = outputs[0][input_ids.shape[-1]:]
return tok.decode(gen_ids, skip_special_tokens=True)
_CODE_BLOCK_RE = re.compile(r"```[a-zA-Z0-9_-]*\n(.*?)```", re.DOTALL)
def _extract_first_code_block(s: str) -> str:
m = _CODE_BLOCK_RE.search(s)
return (m.group(1) if m else s)
def _sanitize_generated_content(path: str | None, fmt: str, text: str) -> str:
s = (text or "").replace("\r\n", "\n").strip()
# Strip common lead-ins & fences
s = re.sub(r"^\s*(here\s+is.*?:|here'?s.*?:)\s*\n", "", s, flags=re.I)
s = _extract_first_code_block(s)
s = s.replace("```", "").strip()
name = (os.path.basename(path) if path else "").lower()
# requirements.txt → keep only valid requirement lines
if name == "requirements.txt":
lines = []
for line in s.splitlines():
t = line.strip()
if not t or t.startswith("#"):
continue
if re.match(r"^[A-Za-z0-9_.-]+(\s*(?:[<>=!]=|===|==|~=)\s*[^#\s]+)?(\s*#.*)?$", t):
lines.append(t)
if not lines:
# hard fallback (good enough for the Flask scaffold)
return "flask\npytest\n"
return "\n".join(lines) + "\n"
return s
def _looks_like_literal_content(path: str | None, fmt: str, instruction: str) -> bool:
"""True if user is giving us the final file body (not 'write ... about ...')."""
instr = (instruction or "").strip()
low = instr.lower()
has_verbs = re.search(r"\b(create|write|generate|explain|tutorial|guide|steps|add|include|document)\b", low)
codey = re.search(r"\b(def |class |from |import |if __name__ == .__main__|@app)\b", instr)
many_newlines = instr.count("\n") >= 1
return (many_newlines and not has_verbs) or bool(codey)
def _has_actionable(steps):
return any((s.get("type") or "").lower() in ACTIONABLE for s in (steps or []))
def _plan_create_file_from_prompt(prompt: str):
p = prompt.strip()
# Pattern A: "create a file NAME at /dir with/about/on CONTENT"
m = re.search(
r"(?:create|make|generate|write)\s+(?:an?\s+)?file\s+([A-Za-z0-9._-]+)"
r"\s+(?:in|at)\s+(/[\w/\.-]+)"
r"(?:\s+(?:with|containing|about|on)\s+(.+))?$",
p, re.I)
if m:
filename, dirpath, about = m.group(1), m.group(2), (m.group(3) or "").strip()
path = f"{dirpath.rstrip('/')}/{filename}"
instruction = about or "Create a very short factual note."
return {
"steps": [
{
"type": "generate_file",
"path": path,
"instruction": instruction, # e.g., "information on Amitabh Bachchan"
"format": "text",
"length": "short" # keep it concise, not an article
},
{
"type": "respond_llm",
"instruction": f"Confirm that '{path}' was created and summarize in one line what you wrote.",
"use_previous": False
}
]
}
# Pattern B: "create a file NAME at /dir" (no content -> empty file)
m = re.search(
r"(?:create|make|generate|write)\s+(?:an?\s+)?file\s+([A-Za-z0-9._-]+)\s+(?:in|at)\s+(/[\w/\.-]+)\s*$",
p, re.I)
if m:
filename, dirpath = m.group(1), m.group(2)
path = f"{dirpath.rstrip('/')}/{filename}"
return {
"steps": [
{"type": "write_file", "path": path, "content": "", "mode": "w"},
{"type": "respond_llm", "instruction": f"Confirm creation of '{path}'.", "use_previous": False}
]
}
return None
def _plan_edit_file_from_prompt(prompt: str):
"""
Detect common 'edit/update/upgrade/modify/replace' intents on a specific file path,
optionally with a second path (e.g., an image to use), and produce a rewrite_file step.
"""
s = prompt.strip()
# Any edit-like verb?
if not re.search(r"\b(edit|update|upgrade|modify|change|replace|append|insert|use|refactor)\b", s, re.I):
return None
# Target file path (absolute or relative like ./, ../), or bare filename.ext
match_paths = list(re.finditer(
r"((?:\./|\../|/)?[\w\-/\.]+?\.(?:html?|txt|md|json|py|js|css|ts|tsx|jsx|scss))",
s, re.I
))
if match_paths:
# Prefer the longest match (so "./test/index.html" wins over "/test/index.html")
target_path = max(match_paths, key=lambda m: m.end()-m.start()).group(1)
else:
# Try a simple filename.ext
m_simple = re.search(r"\b([A-Za-z0-9._-]+\.(?:html?|txt|md|json|py|js|css|ts|tsx|jsx|scss))\b", s, re.I)
if not m_simple:
return None
target_path = m_simple.group(1)
# Optional second path (e.g., image)
img = re.search(r"(/[\w\-/\.]+\.(?:png|jpe?g|gif|svg|webp))", s, re.I)
instruction = prompt.strip()
# If both an HTML file and an image path are present, add a helpful relative-path hint.
if img and re.search(r"\.html?$", target_path, re.I):
img_path = img.group(1)
try:
rel = os.path.relpath(img_path, start=os.path.dirname(target_path))
except Exception:
rel = img_path
instruction += (
f"\n\nNote: Prefer referencing the image via the relative path '{rel}' "
f"(instead of an absolute file path) so it loads when opened locally."
)
steps = [
{"type": "rewrite_file", "path": target_path, "instruction": instruction, "length": "long"},
]
want_show = re.search(r"\b(show|display|print|reveal|dump)\b", s, re.I)
if want_show:
steps.append({"type": "fs", "op": "read", "path": target_path})
steps.append({"type": "respond_llm",
"instruction": f"Show the final contents of '{target_path}'. If it's long, summarize sections and key changes.",
"use_previous": True})
else:
steps.append({"type": "respond_llm",
"instruction": f"Briefly confirm the update to '{target_path}' and how to open it.",
"use_previous": False})
return {"steps": steps}
_QA_PREFIX_RE = re.compile(r'(?:^|\n)\s*question:\s*(.+)\Z', re.IGNORECASE | re.DOTALL)
def _extract_question_from_instruction(instruction: str) -> str:
"""
Pull the user question out of an instruction blob like:
'Answer clearly ... Do NOT repeat the question.\\n\\nQuestion: Who is Ada Lovelace?'
Falls back to the instruction text if no Question: header is present.
Also strips obvious meta preambles like 'Answer clearly...' lines.
"""
instr = instruction or ""
m = _QA_PREFIX_RE.search(instr)
if m:
return m.group(1).strip()
# remove common meta lines the planner adds
cleaned = []
for line in instr.splitlines():
low = line.strip().lower()
if low.startswith(("answer", "instruction", "do not repeat", "don’t repeat", "do n't repeat")):
continue
cleaned.append(line)
q = "\n".join(cleaned).strip()
return q or instr.strip()
def _strip_meta_lines(ans: str) -> str:
"""Remove any stray 'Question:'/'Instruction:'/'Answer:' prefixes the model might echo."""
lines = []
for ln in (ans or "").splitlines():
low = ln.strip().lower()
if low.startswith(("question:", "instruction:", "answer:")):
continue
lines.append(ln)
return "\n".join(lines).strip()
# ──────────────────────────────────────────────
# Planner tool catalog + schema (module-scope)
# ──────────────────────────────────────────────
TOOLS = """
TOOLS (choose as few as possible to satisfy the request):
1) fs
- A generic filesystem tool.
- Fields:
{"type":"fs","op":"list|read|write|append|mkdir|remove|move|copy|exists|glob",
"path":"<abs or relative path>",
"content":"<text>", "to":"<dest path>", "pattern":"<glob pattern>"}
- Use cases:
• "ls /path", "show/list contents of DIR" → {"type":"fs","op":"list","path":"/path"}
• "remove/delete file /a/b.txt" → {"type":"fs","op":"remove","path":"/a/b.txt"}
• "show /a/b.txt" / "cat file" → {"type":"fs","op":"read","path":"/a/b.txt"}
2) shell
- Run an OS command when no dedicated tool exists.
- Prefer fs over shell for file management.
- Use per-OS mapping via {"cmd": {"linux":"…","darwin":"…","windows":"…"}}.
3) read_file / write_file / append_file / mkdirs / list_dir
- Legacy, still allowed; prefer fs unless the user explicitly asked for these.
4) python
- For quick local computations or tiny scripts.
5) generate_file / generate_tree / generate_large_file / rewrite_file
- For content/code generation and edits.
Always end with ONE summarize step:
{"type":"respond_llm","instruction":"Briefly confirm what happened or show the results.","use_previous":true}
"""
PLANNER_SCHEMA = (
"You are a CLI automation planner that MUST return ONLY a single JSON object.\n"
"NO prose. NO markdown. JSON ONLY.\n\n"
+ TOOLS +
"\nSchema:\n"
"{\n"
' "steps": [ <one or more tool steps from TOOLS, and finally exactly one respond/respond_llm> ]\n'
"}\n"
"Rules:\n"
"- If the request requires inspecting or changing the system/files, you MUST use a tool step (not just respond).\n"
"- Prefer fs for file/directory operations.\n"
"- Keep steps minimal and directly useful.\n"
"- Include timeouts/cwd/env on shell only when needed.\n"
"- End with exactly one respond/respond_llm (use_previous=true when summarizing gathered output).\n"
"\nExamples:\n"
"USER: ls /tmp\n"
'{"steps":[{"type":"fs","op":"list","path":"/tmp"},{"type":"respond_llm","instruction":"Summarize directory contents.","use_previous":true}]}\n'
"USER: what are the contents of the directory /var/log\n"
'{"steps":[{"type":"fs","op":"list","path":"/var/log"},{"type":"respond_llm","instruction":"List entries clearly.","use_previous":true}]}\n'
"USER: remove file /Users/alex/test.html\n"
'{"steps":[{"type":"fs","op":"remove","path":"/Users/alex/test.html"},{"type":"respond_llm","instruction":"Confirm deletion.","use_previous":false}]}\n'
)
# ──────────────────────────────────────────────
# 2) Planning logic — strict JSON with schema + robust parse
# ──────────────────────────────────────────────
def plan_actions_from_prompt(model_pipe, prompt, context=""):
# Pre-parsed quick path: explicit "create file ..." phrasing
pre_edit = _plan_edit_file_from_prompt(prompt)
if pre_edit:
return pre_edit
pre = _plan_create_file_from_prompt(prompt)
if pre:
return pre
s = prompt.lower().strip()
# ── NEW: Heuristic for "create folder here + create file" (your case) ──
# examples: "create a folder named test here ... and create a test.html ..."
folder_re = re.search(
r"(?:create|make|mkdir)\s+(?:a\s+)?(?:folder|directory)\s+(?:named|called)?\s*([A-Za-z0-9._-]+)",
prompt, re.I
)
file_re = re.search(
r"(?:create|make|generate|write)\s+(?:an?\s+)?([A-Za-z0-9._-]+\.(?:html?|txt|md|json|py|js|css))",
prompt, re.I
)
# optional absolute base path like "in /tmp" or "at /var/www"
abs_base_re = re.search(r"\b(?:in|at)\s+(/[\w/\.-]+)", prompt, re.I)
# detect "here" wording
here_re = re.search(r"\b(here|in\s+(?:this|the)\s+directory|in\s+current\s+dir(?:ectory)?|in\s+\.)\b", s)
if folder_re or file_re:
folder = folder_re.group(1) if folder_re else None
filename = file_re.group(1) if file_re else None
base = abs_base_re.group(1).rstrip("/") if abs_base_re else "."
steps = []
# Create folder if requested (relative to base unless absolute provided above)
if folder:
folder_path = f"{base}/{folder}" if base != "." else f"./{folder}"
steps.append({"type": "mkdirs", "paths": [folder_path]})
# Create file if requested
# Create file if requested
if filename:
# Detect explicit file path in prompt (if present, respect it)
explicit_file_path = re.search(
r"(/[\w/\.-]+\.(?:html?|txt|md|json|py|js|css))", prompt, re.I
)
if explicit_file_path:
path = explicit_file_path.group(1)
elif folder:
# Default to placing the file inside the newly created folder
path = (f"{base}/{folder}/{filename}") if base != "." else f"./{folder}/{filename}"
else:
path = (f"{base}/{filename}") if base != "." else f"./{filename}"
# Build a helpful instruction from the prompt
wants_pics = bool(re.search(r"\b(pictures?|images?|gallery|photos?)\b", s))
fmt = "html" if filename.lower().endswith((".html", ".htm")) else "text"
length = "long" if fmt == "html" else "medium"
instruction = prompt.strip()
# If user asked for pictures and it's HTML, steer to a nice sample gallery
if fmt == "html" and wants_pics:
instruction = (
"Create a single-file, modern HTML5 page with embedded <style> CSS: "
"a clean header, hero section, and a responsive image grid (6–9 images). "
"Use web-safe fonts or a Google Fonts import, CSS grid/flex, subtle shadows, "
"and hover effects. Use external placeholder photos (e.g., Unsplash image URLs) "
"with descriptive alt text and loading='lazy'. No JS required."
)
steps.append({
"type": "generate_file",
"path": path,
"instruction": instruction,
"format": fmt,
"length": length
})
# Conclude with a short confirmation
steps.append({
"type": "respond_llm",
"instruction": "Confirm what was created and how to open the HTML in a browser.",
"use_previous": False
})
return {"steps": steps}
# ── Heuristic for “save it at/to/in <path.ext>” or “… at/in <path.ext>”
save_any = re.search(
r"\b(?:save\s+(?:it|this|the\s+\w+)?\s*)?(?:at|to|in)\s+((?:\./|\../|/)?[\w/\.\-]+?\.(?:txt|md|html?|json|py|js|css))",
prompt, re.I
)
if save_any:
path = save_any.group(1)
lower = path.lower()
if lower.endswith((".html",".htm")): fmt = "html"
elif lower.endswith(".md"): fmt = "markdown"
else: fmt = "text"
# honor "2000 words" or "2000-word"
wants_long = bool(re.search(r"\b(\d{3,4})\s*[- ]?\s*words?\b", prompt, re.I))
length = "long" if wants_long else "medium"
return {
"steps": [
{"type":"generate_file","path":path,"instruction":prompt.strip(),"format":fmt,"length":length},
{"type":"respond_llm","instruction":f"Confirm that '{path}' was written and how to open it.","use_previous":True}
]
}
# ── Light intent: reverse DNS on IPs ──
ip_match = re.search(r"\b(\d{1,3}(?:\.\d{1,3}){3})\b", prompt)
if ip_match and re.search(r"\b(dns|dns\s*check|reverse\s*dns|ptr|rdns|hostname)\b", s):
ip = ip_match.group(1)
steps = [{
"type": "shell",
"cmd": {
"linux": f"dig -x {ip} +short",
"darwin": f"dig -x {ip} +short",
"windows": f"nslookup -type=PTR {ip}"
},
"requires": {"linux": ["dig"], "darwin": ["dig"]},
"timeout": 10
}]
if re.search(r"\b(whois|owner|asn|org|organisation|organization|provider)\b", s):
steps.append({
"type": "shell",
"cmd": {
"linux": f"whois {ip} | head -n 80",
"darwin": f"whois {ip} | head -n 80",
"windows": f"whois {ip}"
},
"env": {"PAGER":"cat","LESS":"-R"},
"requires": {"linux": ["whois"], "darwin": ["whois"], "windows": ["whois"]},
"timeout": 10
})
return {"steps": steps}
# Quick path: "node and npm versions"
if re.search(r"\bnode\b.*\bnpm\b.*\bversion", s) or re.search(r"\bversions?\b.*\bnode\b.*\bnpm\b", s):
return {"steps":[
{"type":"shell","cmd":{"linux":"node -v && npm -v","darwin":"node -v && npm -v","windows":"node -v & npm -v"}},
{"type":"respond_llm","instruction":"Report the exact Node.js and npm versions from the previous output (no guessing).","use_previous":True}
]}
# Quick path: count non-comment entries in /etc/hosts
if ("/etc/hosts" in prompt) and re.search(r"\bnon[- ]?comment\b", s) and re.search(r"\bhow\s+many\b", s):
return {"steps":[
{"type":"python","code":
"count=0\n"
"with open('/etc/hosts') as f:\n"
" for line in f:\n"
" s=line.strip()\n"
" if s and not s.startswith('#'):\n"
" count+=1\n"
"print(count)\n"},
{"type":"respond_llm","instruction":"Tell me the number you just computed.","use_previous":True}
]}
# Quick path: scaffold a minimal Flask app at <dir>
m_flask = re.search(r"\bscaffold\b.*\bflask\b.*?(?:at|in)\s+((?:\./|\../|/)?[\w/\.-]+)", prompt, re.I)
if m_flask:
base_dir = m_flask.group(1) if m_flask.group(1) else "./flask_demo"
return {"steps":[
{"type":"generate_tree","base":base_dir,"files":[
{"path":"requirements.txt","format":"text","length":"short","instruction":"flask\npytest\n"},
{"path":"app.py","format":"code:python","length":"medium","instruction":
"from flask import Flask\napp=Flask(__name__)\n@app.get('/')\n"
"def hello():\n return 'Hello, Flask!'\n\nif __name__=='__main__':\n"
" app.run(host='0.0.0.0',port=5000)"},
{"path":"tests/test_app.py","format":"code:python","length":"short","instruction":
"from app import app\n\ndef test_root():\n c=app.test_client(); r=c.get('/')\n assert r.status_code==200"},
{"path":"README.md","format":"markdown","length":"short","instruction":
"# Flask demo\n\n## Setup\n\n```\npython3 -m venv .venv\n. .venv/bin/activate\npip install -r requirements.txt\n```\n\n## Run\n```\nflask --app app run --host=0.0.0.0 --port=5000\n```\n"},
{"path":".gitignore","format":"text","length":"short","instruction":"__pycache__/\n.venv/\n"}
]},
{"type":"shell","cmd":{
"linux":"python3 -m venv .venv && . .venv/bin/activate && pip install -r requirements.txt",
"darwin":"python3 -m venv .venv && . .venv/bin/activate && pip install -r requirements.txt",
"windows":"python -m venv .venv && .\\.venv\\Scripts\\pip install -r requirements.txt"
},"cwd":base_dir},
{"type":"shell","cmd":{
"linux":"FLASK_APP=app flask run --host=0.0.0.0 --port=5000",
"darwin":"flask --app app run --host=0.0.0.0 --port=5000",
"windows":"set FLASK_APP=app && flask run --host=0.0.0.0 --port=5000"
},"cwd":base_dir},
{"type":"respond_llm","instruction":"Confirm scaffold and how to run locally.","use_previous":True}
]}
# Quick path: scaffold a Python CLI (pyproject + console_scripts)
m_cli = re.search(
r"\b(cli|command[- ]line)\b.*\b(pyproject\.toml|console[_-]?scripts|entry\s*point)\b.*?(?:at|in)\s+((?:\./|\../|/)?[\w/\.-]+)",
prompt, re.I
)
if m_cli:
base_dir = m_cli.group(3) if m_cli.group(3) else "./time_cli"
return {"steps": [
{"type": "generate_tree", "base": base_dir, "files": [
{"path": "pyproject.toml", "format": "text", "length": "short", "instruction":
"[build-system]\nrequires = [\"hatchling\"]\nbuild-backend = \"hatchling.build\"\n\n"
"[project]\nname = \"time-cli\"\nversion = \"0.1.0\"\n"
"description = \"Prints local and UTC time\"\nreadme = \"README.md\"\n"
"requires-python = \">=3.8\"\ndependencies = []\n\n"
"[project.scripts]\n"
"time-cli = \"time_cli.cli:main\"\n\n"
"[tool.pytest.ini_options]\naddopts = \"-q\"\n"},
{"path": "src/time_cli/__init__.py", "format": "code:python", "length": "short", "instruction":
"__all__ = [\"__version__\"]\n__version__ = \"0.1.0\"\n"},
{"path": "src/time_cli/cli.py", "format": "code:python", "length": "short", "instruction":
"from datetime import datetime, timezone\n\n"
"def main() -> None:\n"
" local = datetime.now()\n"
" utc = datetime.now(timezone.utc)\n"
" print(f\"Local time: {local.strftime('%Y-%m-%d %H:%M:%S')}\")\n"
" print(f\"UTC time: {utc.strftime('%Y-%m-%d %H:%M:%S')}\")\n\n"
"if __name__ == \"__main__\":\n"
" main()\n"},
{"path": "tests/test_cli.py", "format": "code:python", "length": "short", "instruction":
"from io import StringIO\nimport contextlib\nfrom time_cli import cli\n\n"
"def test_output():\n"
" buf = StringIO()\n"
" with contextlib.redirect_stdout(buf):\n"
" cli.main()\n"
" out = buf.getvalue()\n"
" assert \"Local time:\" in out and \"UTC time:\" in out\n"},
{"path": "README.md", "format": "markdown", "length": "short", "instruction":
"# Time CLI\n\n"
"A tiny CLI that prints local and UTC time.\n\n"
"## Install & run\n\n"
"```bash\npython3 -m venv .venv\n. .venv/bin/activate\npip install -U pip\npip install -e .\npip install pytest\npytest\n"
"time-cli\n```\n"},
{"path": ".gitignore", "format": "text", "length": "short", "instruction":
"__pycache__/\n.venv/\n*.pyc\n*.pyo\n*.pytest_cache/\n"}
]},
{"type": "shell", "cmd": {
"linux": "python3 -m venv .venv && . .venv/bin/activate && pip install -U pip && pip install -e . && pip install pytest",
"darwin": "python3 -m venv .venv && . .venv/bin/activate && pip install -U pip && pip install -e . && pip install pytest",
"windows": "python -m venv .venv && .\\.venv\\Scripts\\pip install -U pip && .\\.venv\\Scripts\\pip install -e . && .\\.venv\\Scripts\\pip install pytest"
}, "cwd": base_dir},
{"type": "shell", "cmd": {
"linux": "pytest",
"darwin": "pytest",
"windows": ".\\.venv\\Scripts\\pytest"
}, "cwd": base_dir},
{"type": "shell", "cmd": {
"linux": "time-cli",
"darwin": "time-cli",
"windows": ".\\.venv\\Scripts\\time-cli"
}, "cwd": base_dir},
{"type": "respond_llm", "instruction": "Confirm scaffold and how to run locally.", "use_previous": True}
]}
# Quick path: "open browser and search ..." / "google '...'"
m_web = (re.search(r'\b(open|launch)\s+(?:a\s+)?browser.*?(?:search|google|bing|duckduckgo)?\s*(?:for|about)?\s*"([^"]+)"', prompt, re.I)
or re.search(r'\b(search|google|bing|duckduckgo)\b.*?"([^"]+)"', prompt, re.I))
if m_web:
from urllib.parse import quote_plus
query = m_web.group(2)
url = f"https://www.google.com/search?q={quote_plus(query)}"
return {"steps": [
{"type":"shell","cmd":{
"linux": f'xdg-open "{url}"',
"darwin": f'open "{url}"',
"windows": f'start "" "{url}"'
}},
{"type":"respond_llm",
"instruction": f"Tell the user their default browser was opened to a Google search for “{query}”. If it didn’t open, provide the URL shown in context.",
"use_previous": False,
"context": url}
]}
# App scaffold hint (nudges the model toward generate_tree + setup commands)
APP_HINT = re.search(
r"\b(flask|fastapi|django|react|next\.js|node|express|go\b|rust\b|java\b|spring|kotlin|swiftui|vue|svelte|angular)\b",
s
)
if APP_HINT:
context = (context or "") + (
"\n\nPLANNING_HINT: For apps, return generate_tree with a clean project layout, "
"plus shell steps to install deps and run dev server/tests."
)
# Normal LLM planning — always define SCHEMA
SCHEMA = (
"You are a CLI automation planner that MUST return ONLY a single JSON object.\n"
"NO prose. NO markdown. JSON ONLY.\n\n"
"Schema:\n"
"{\n"
' "steps": [\n'
' {"type":"respond","text":"<final answer text>"} |\n'
' {"type":"respond_llm","instruction":"<what to write>", "use_previous":true, "context":"<optional extra>"} |\n'
' {"type":"shell","cmd":"<command>","cwd":"<optional path>","timeout":<seconds>,"env":{"K":"V"},"requires":{"linux":["..."],"darwin":["..."],"windows":["..."]}} |\n'
' {"type":"read_file","path":"<path>"} |\n'
' {"type":"write_file","path":"<path>","content":"<text>","mode":"w|a"} |\n'
' {"type":"edit_file","path":"<path>","content":"<patch or full text>"} |\n'
' {"type":"append_file","path":"<path>","content":"<text>"} |\n'
' {"type":"list_dir","path":"<path>"} |\n'
' {"type":"python","code":"<python code>"} |\n'
' {"type":"generate_file","path":"<path>","instruction":"<what to write>","format":"text|code:<lang>|markdown|html","length":"short|medium|long|xl"} |\n'
' {"type":"mkdirs","paths":["<dir>", "..."]} |\n'
' {"type":"generate_tree","base":"<dir>","files":[{"path":"<rel path>","instruction":"...","format":"text|code:<lang>|html|markdown","length":"short|medium|long|xl"}]} |\n'
' {"type":"generate_large_file","path":"<path>","chunks":[{"instruction":"...","length":"short|medium|long|xl"}, "..."]}\n'
' {"type":"rewrite_file","path":"<path>","instruction":"<how to change the file>","length":"short|medium|long|xl"} |\n'
" ]\n"
"}\n"
"Rules:\n"
"- The JSON MUST include a non-empty 'steps' array.\n"
"- For imperative requests (e.g., create/make/run/write), prefer executable steps over explanations.\n"
"- When reading/inspecting data, gather with read_file/shell/python, then ONE respond_llm(use_previous=true).\n"
"- Use 'respond' only when you include the actual final answer text (no placeholders).\n"
"- Prefer 'generate_tree' for apps/libraries: create a real multi-file project layout (modules/packages, config, tests).\n"
"- For big files, use 'generate_large_file' (or multiple append_file) to write in chunks.\n"
"- Use format 'code:<lang>' (e.g., code:python, code:javascript, code:go) for code files; no backticks.\n"
"- Add shell steps to set up and run the project (pip/npm/etc.).\n"
"- Keep steps minimal and directly useful.\n"
)
USER_BLOCK = f"USER_INSTRUCTION: {prompt}\nSERVER_OS: {SERVER_OS}\nCONTEXT: {context}\nRETURN JSON NOW:"
raw = llm_chat(model_pipe, PLANNER_SCHEMA, USER_BLOCK)
if os.getenv("AGENT_DEBUG") == "1":
print("\n=== RAW LLM OUTPUT (pass1) ===\n", raw, "\n==============================\n", flush=True)
try:
plan = extract_first_json_object(raw)
if not isinstance(plan, dict) or not isinstance(plan.get("steps", []), list) or len(plan["steps"]) == 0:
raise ValueError("empty or invalid 'steps'")
if not _has_actionable(plan.get("steps")):
h = _plan_create_file_from_prompt(prompt)
if h:
return h
e = _plan_edit_file_from_prompt(prompt) # ← NEW fallback for edit/upgrade requests
if e:
return e
return plan
except Exception:
STRICT = PLANNER_SCHEMA + "\nMUST include at least one executable step in 'steps' (not only 'respond')."
raw2 = llm_chat(model_pipe, STRICT, USER_BLOCK)
if os.getenv("AGENT_DEBUG") == "1":
print("\n=== RAW LLM OUTPUT (pass2) ===\n", raw2, "\n==============================\n", flush=True)
try:
plan2 = extract_first_json_object(raw2)
if isinstance(plan2, dict) and isinstance(plan2.get("steps", []), list) and len(plan2["steps"]) > 0:
return plan2
except Exception:
pass
# Fallbacks for common imperative phrasing (expanded to include 'folder')
if any(k in s for k in ["make a directory", "create a directory", "create a folder", "make a folder", "mkdir"]):
m = re.search(r"(?:named|called)?\s*([A-Za-z0-9._-]+)", prompt, re.I)
if m:
name = m.group(1)
path = f"./{name}"
ps_path = path.replace('"', '`"')
return {"steps": [{
"type": "shell",
"cmd": {
"linux": f'mkdir -p "{path}"',
"darwin": f'mkdir -p "{path}"',
"windows": f'powershell -NoProfile -Command "New-Item -ItemType Directory -Path \\"{ps_path}\\" -Force | Out-Null"'
}
}]}
m3 = re.search(r"(?:write|create|generate)\s+(.+?)\s+in\s+([A-Za-z0-9._-]+)\s+at\s+(/[\w\-/]+)", prompt, re.I)
if m3:
what, filename, base = m3.groups()
return {"steps": [{
"type": "generate_file",
"path": f"{base.rstrip('/')}/{filename}",
"instruction": what.strip(),
"format": "text",
"length": "medium"
}]}
# Final safe fallback: let the model answer
return {"steps": [{"type": "respond_llm", "instruction": f"Answer: {prompt}", "use_previous": False}]}
def ensure_concluding_response(plan: dict, user_prompt: str) -> dict:
"""
If a plan has no concluding respond/respond_llm step, append a generic
'respond_llm' that answers the user's prompt using previous step outputs.
This is command-agnostic and fixes cases like 'read file ... tell me ...'
where the model forgot to add a summarization step.
"""
steps = plan.get("steps", [])
if not isinstance(steps, list):
steps = []
plan["steps"] = steps
# If there is already a respond/respond_llm step, keep as-is.
for s in steps:
t = (s.get("type") or "").lower()
if t in {"respond", "respond_llm"}:
# only default to True if the step didn't specify a preference
if t == "respond_llm" and ("use_previous" not in s):
s["use_previous"] = True
return plan
# Otherwise, append a concluding answer step that uses prior context.
steps.append({
"type": "respond_llm",
"instruction": f"Answer the user's request: {user_prompt}",
"use_previous": True
})
return plan
def _likely_needs_io(user_text: str) -> bool:
# Generic, non-brittle signal: absolute paths, path-like tokens, or classic IO verbs.
s = user_text.lower()
pathish = bool(re.search(r"(/|\\)[^\\s]+", user_text))
verbs = any(v in s for v in [
"ls","list","contents","show","cat","read","remove","delete","mkdir",
"create file","write","append","copy","move","save",
"open browser","open url","search","google","bing","duckduckgo","browse"
])
return pathish or verbs
def _force_actionable_if_needed(model_pipe, user_prompt: str, first_plan: dict, context: str):
if _has_actionable(first_plan.get("steps")):
return first_plan
if not _likely_needs_io(user_prompt):
return first_plan
# Re-ask with a hard constraint
FORCE = PLANNER_SCHEMA + "\nYour previous plan lacked a tool step. The user request needs system I/O.\nReturn a plan that USES TOOLS (e.g., fs), then a single respond_llm."
raw = llm_chat(model_pipe, FORCE, f"USER_INSTRUCTION: {user_prompt}\nSERVER_OS: {SERVER_OS}\nCONTEXT:{context}\nRETURN JSON NOW:")
try:
plan2 = extract_first_json_object(raw)
if _has_actionable(plan2.get("steps")):
return plan2
except Exception:
pass
return first_plan
model = load_llm()
@app.route("/gen", methods=["POST"])
@require_api_key
def gen():
payload = request.json or {}
fmt = payload.get("format","text")
instruction = payload.get("instruction","")
length = payload.get("length","medium")
# If caller already handed us the exact file body, just return it.
if _looks_like_literal_content(None, fmt, instruction):
content = _sanitize_generated_content(None, fmt, instruction)
return jsonify({"content": content})
# Otherwise generate deterministically and sanitize.
lang_hint = ""
if isinstance(fmt, str) and fmt.startswith("code:"):
lang_hint = f"\nLanguage: {fmt.split(':',1)[1]}"
fmt = "text"
sys_prompt = "Return ONLY the exact file content asked for. No explanations, no code fences, no headers."
size_hint = {"short":400,"medium":1200,"long":2400,"xl":4800}.get(length,1200)
user_prompt = f"Format: {fmt}{lang_hint}\nInstruction: {instruction}\n"
raw = llm_generate_text_exact(model, sys_prompt, user_prompt, max_new_tokens=size_hint)
content = _sanitize_generated_content(None, fmt, raw)
return jsonify({"content": content})
def _get_cmd_string(cmd_value):
if isinstance(cmd_value, str):
return cmd_value
if isinstance(cmd_value, dict):
return (cmd_value.get(SERVER_OS)
or (cmd_value.get("unix") if SERVER_OS in ("linux","darwin") else None)
or cmd_value.get("default")
or next((v for v in cmd_value.values() if isinstance(v,str) and v.strip()), ""))
return ""
def _strip_browser_opens(plan: dict, prompt: str) -> dict:
# If it's a plain informational request, drop 'open/start http...' shell steps
if _likely_needs_io(prompt):
return plan
steps = plan.get("steps", [])
for s in steps:
if (s.get("type") == "shell"):
cmd = _get_cmd_string(s.get("cmd"))
if re.search(r"\b(open|start)\b.+https?://", cmd):
return {"steps":[{"type":"respond_llm",
"instruction":f"Answer clearly in 2–4 sentences. Do NOT repeat the question.\n\nQuestion: {prompt.strip()}",
"use_previous":False}]}
return plan
def _ensure_qa_instruction(plan: dict, prompt: str) -> dict:
"""
If there are no actionable steps (fs/shell/etc.) and the plan ends in a respond step,
turn that into an explicit 'answer the question' instruction (no echo).
"""
steps = plan.get("steps") or []
actionable_before = any(
(s.get("type","").lower() in ACTIONABLE)
for s in steps
if s.get("type","").lower() not in {"respond","respond_llm"}
)
if not actionable_before and steps:
last = steps[-1]
last["type"] = "respond_llm"
last["instruction"] = (
"Answer clearly in 2–4 sentences. Do NOT repeat the question. "
"Do NOT claim to have opened a browser or searched the web.\n\n"
f"Question: {prompt.strip()}"
)
last["use_previous"] = False
return plan
@app.route("/infer", methods=["POST"])
@require_api_key
def infer():
payload = request.json or {}
prompt = payload.get("prompt", "")
context = payload.get("context", "")
plan = plan_actions_from_prompt(model, prompt, context)
plan = _force_actionable_if_needed(model, prompt, plan, context)
plan = _strip_browser_opens(plan, prompt)
# ⬇️ ADD THIS LINE
plan = _ensure_qa_instruction(plan, prompt)
plan = ensure_concluding_response(plan, prompt)
return jsonify({"plan": plan})
def resolve_cmd_by_os(cmd_value):
"""
Accepts either a string or a dict of {os_name: cmd}.
Picks the right command for SERVER_OS, with sensible fallbacks.
"""
if isinstance(cmd_value, str):
return cmd_value
if isinstance(cmd_value, dict):
# Exact match first
c = cmd_value.get(SERVER_OS)
if c:
return c
# 'unix' fallback for linux/darwin
if SERVER_OS in ("linux", "darwin") and cmd_value.get("unix"):
return cmd_value["unix"]
# 'default' fallback
if cmd_value.get("default"):
return cmd_value["default"]
# last resort: first non-empty string value
for v in cmd_value.values():
if isinstance(v, str) and v.strip():
return v
raise ValueError("Invalid 'cmd' in shell step: expected string or {os: cmd} map.")
def resolve_requires_by_os(req_value):
if not req_value:
return []
if isinstance(req_value, str):
return [req_value]
if isinstance(req_value, list):
return [x for x in req_value if isinstance(x, str)]
if isinstance(req_value, dict):
v = req_value.get(SERVER_OS)
if v is None and SERVER_OS in ("linux", "darwin"):
v = req_value.get("unix")
if v is None:
v = req_value.get("default")
if isinstance(v, str):
return [v]
if isinstance(v, list):
return [x for x in v if isinstance(x, str)]
return []
def _which(cmd: str) -> bool:
return bool(shutil.which(cmd))
def _guess_tools_from_cmd(cmd: str) -> list[str]:
KNOWN = {"dig","nmap","whois","traceroute","nslookup","curl","wget","jq","git",
"python3","python","pip","pip3","node","npm"}
try:
first = shlex.split(cmd)[0] if cmd else ""
except Exception:
first = (cmd or "").strip().split(" ", 1)[0]
return [first] if first in KNOWN else []
def _detect_linux_pkg_mgr():
try:
with open("/etc/os-release","r") as f:
data = f.read().lower()
def has(*keys): return any(k in data for k in keys)
if has("id_like=debian","id=debian","id=ubuntu","ubuntu"): return "apt"
if has("id=fedora","id_like=fedora","id=rhel","centos","rocky","almalinux","amzn"):
return "dnf" if shutil.which("dnf") else "yum"
if has("id_like=alpine","id=alpine"): return "apk"
if has("id=arch","id_like=arch"): return "pacman"
if has("opensuse","sles","suse"): return "zypper"
except Exception:
pass
for pm in ("apt","dnf","yum","apk","pacman","zypper"):
if shutil.which(pm): return pm
return None
_TOOL_PKG_MAP = {
"dig": {"apt":"dnsutils","dnf":"bind-utils","yum":"bind-utils","apk":"bind-tools","pacman":"bind","zypper":"bind-utils"},
"nslookup": {"apt":"dnsutils","dnf":"bind-utils","yum":"bind-utils","apk":"bind-tools","pacman":"bind","zypper":"bind-utils"},
"whois": {"apt":"whois","dnf":"whois","yum":"whois","apk":"whois","pacman":"whois","zypper":"whois"},
"nmap": {"apt":"nmap","dnf":"nmap","yum":"nmap","apk":"nmap","pacman":"nmap","zypper":"nmap"},
"traceroute": {"apt":"traceroute","dnf":"traceroute","yum":"traceroute","apk":"traceroute","pacman":"traceroute","zypper":"traceroute"},
"python3": {"apt":"python3","dnf":"python3","yum":"python3","apk":"python3","pacman":"python","zypper":"python3"},
"pip": {"apt":"python3-pip","dnf":"python3-pip","yum":"python3-pip","apk":"py3-pip","pacman":"python-pip","zypper":"python3-pip"},
"node": {"apt":"nodejs","dnf":"nodejs","yum":"nodejs","apk":"nodejs","pacman":"nodejs","zypper":"nodejs"},
"npm": {"apt":"npm","dnf":"npm","yum":"npm","apk":"npm","pacman":"npm","zypper":"npm"},
}
def _pkg_for_tool(tool: str, pm: str) -> str:
return _TOOL_PKG_MAP.get(tool.lower(), {}).get(pm, tool)
def _install_missing_tools(tools: list[str]) -> tuple[bool,str,list[str]]:
if SERVER_OS != "linux":
return False, "Auto-install only supported on Linux.", []
pm = _detect_linux_pkg_mgr()
if not pm:
return False, "Could not detect Linux package manager.", []
pkgs = [_pkg_for_tool(t, pm) for t in tools]
if pm == "apt":
cmds = ["apt-get update", "apt-get install -y " + " ".join(pkgs)]
elif pm in ("dnf","yum"):
cmds = [f"{pm} -y install " + " ".join(pkgs)]
elif pm == "apk":
cmds = ["apk add --no-cache " + " ".join(pkgs)]
elif pm == "pacman":
cmds = ["pacman -Sy --noconfirm " + " ".join(pkgs)]
elif pm == "zypper":
cmds = ["zypper -n install " + " ".join(pkgs)]
else:
return False, f"Unsupported package manager: {pm}", []
log = []
for c in cmds:
proc = subprocess.run(c, shell=True, capture_output=True, text=True)
log.append(f"$ {c}\n{proc.stdout}{proc.stderr}")
if proc.returncode != 0:
return False, "\n".join(log), []
return True, "\n".join(log), pkgs
def _suggest_install_cmd(tools: list[str]) -> str:
if SERVER_OS == "linux":
pm = _detect_linux_pkg_mgr()
if pm:
pkgs = " ".join([_pkg_for_tool(t, pm) for t in tools])
if pm == "apt": return f"sudo apt-get update && sudo apt-get install -y {pkgs}"
if pm in ("dnf","yum"): return f"sudo {pm} -y install {pkgs}"
if pm == "apk": return f"sudo apk add --no-cache {pkgs}"
if pm == "pacman": return f"sudo pacman -Sy --noconfirm {pkgs}"
if pm == "zypper": return f"sudo zypper -n install {pkgs}"
return "Install the required tools with your distro's package manager."
if SERVER_OS == "darwin":
return "brew install " + " ".join(tools) + " # Requires Homebrew"
if SERVER_OS == "windows":
if shutil.which("winget"): return "winget install " + " ".join(tools)
if shutil.which("choco"): return "choco install -y " + " ".join(tools)
return "Install the tools manually or via winget/choco."
@app.route("/execute", methods=["POST"])
@require_api_key
def execute():
def collect_text_context(results_so_far: list[dict]) -> str:
chunks = []
for r in results_so_far:
t = r.get("type")
if t == "read_file":
chunks.append(r.get("content", ""))
elif t == "shell":
out = (r.get("stdout") or "") + ("\n" + r.get("stderr") if r.get("stderr") else "")
if out.strip():
chunks.append(out)
elif t == "python":
if r.get("stdout", "").strip():
chunks.append(r["stdout"])
elif t == "list_dir":
ents = r.get("entries", [])
if ents:
chunks.append("\n".join(ents))
elif t == "fs":
op = r.get("op")
if op == "read":
chunks.append(r.get("content", ""))
elif op == "exists":
chunks.append(f"EXISTS {r.get('path')}: {r.get('exists')}")
elif op == "glob":
matches = r.get("matches", []) or []
patt = r.get("pattern", "")
header = f"GLOB {patt}\nCOUNT: {len(matches)}"
body = ("\n".join(matches)) if matches else ""
chunks.append(f"{header}\n{body}".strip())
elif op == "list":
entries = r.get("entries", []) or []
p = r.get("path", "")
header = f"LIST {p}\nCOUNT: {len(entries)}"
body = ("\n".join(entries)) if entries else ""
chunks.append(f"{header}\n{body}".strip())
return "\n\n".join([c for c in chunks if c.strip()])
plan = (request.json or {}).get("plan", {})
steps = plan.get("steps", [])
results = []
for idx, step in enumerate(steps, 1):
t = step.get("type")
started = time.time()
try:
if t == "respond_llm":
instruction = step.get("instruction", "").strip() or "Provide a clear, helpful answer."
use_prev = bool(step.get("use_previous", True))
extra_ctx = step.get("context", "")
ctx = extra_ctx
if use_prev:
prev_text = collect_text_context(results)
if prev_text:
ctx = (ctx + "\n\n" + prev_text).strip() if ctx else prev_text
# --- Key change: extract the actual question and send ONLY that as the user message ---
question = _extract_question_from_instruction(instruction)
if ctx:
sys_prompt = (
"You are a precise assistant. Use ONLY the provided context; do not guess. "
"If the answer is not present, say 'Insufficient data.' "
"Answer in 2–4 sentences and do NOT repeat or quote the question."
)
user_prompt = f"{question}\n\n--- Context ---\n{ctx}"
else:
sys_prompt = (
"You are a precise assistant. Answer the question directly in 2–4 sentences. "
"Do NOT repeat or quote the question. "
"Do NOT claim to have opened a browser, clicked anything, or searched the web."
)
user_prompt = question
# === Generate an initial answer (so later checks have a value) ===
answer = llm_generate_text(model, sys_prompt, user_prompt, max_new_tokens=300).strip()
# --- Deterministic extractors / fast-paths before LLM ---
import re as _re
low_inst = (instruction or "").lower()
q_low = (question or "").lower()
if ctx:
# 1) Node + npm versions (from: `node -v && npm -v`)
if ("node" in low_inst and "npm" in low_inst and
("version" in low_inst or "versions" in low_inst)):
vers = _re.findall(r"(?:^|\s)(v?\d+\.\d+\.\d+)(?=\s|$)", ctx)
if len(vers) >= 2:
node_v = vers[0] if vers[0].startswith("v") else "v" + vers[0]
npm_v = vers[1].lstrip("v")
res_obj = {"type": "respond", "text": f"Node.js {node_v}; npm {npm_v}.", "ok": True}
results.append(res_obj); continue
# 2) Simple numeric answer (e.g., Python computed count)
if ("number" in low_inst or "how many" in q_low) and _re.fullmatch(r"\s*\d+\s*", (ctx or "")):
n = (ctx or "").strip()
res_obj = {"type": "respond", "text": n, "ok": True}
results.append(res_obj); continue
# 3) Reverse DNS (PTR) — extract the first hostname-looking token from context
if ("reverse dns" in low_inst or "ptr" in low_inst or "rdns" in low_inst or "reverse dns" in q_low or "ptr" in q_low):
m_host = _re.search(r"([a-z0-9](?:[a-z0-9\-]*[a-z0-9])?(?:\.[a-z0-9](?:[a-z0-9\-]*[a-z0-9])?)+\.?)", ctx, _re.I)
if m_host:
host = m_host.group(1)
res_obj = {"type": "respond", "text": f"PTR → {host}", "ok": True}
results.append(res_obj); continue
# 4) WHOIS — show first lines we captured; keep it short but useful
if ("whois" in low_inst or "whois" in q_low) and ctx.strip():
lines = [ln for ln in ctx.splitlines() if ln.strip()]
# keep the most informative early lines
head = "\n".join(lines[:25]) if lines else ""
text = head or "WHOIS output was empty."
res_obj = {"type": "respond", "text": text, "ok": True}
results.append(res_obj); continue
# 5) “List/show/print” results when we already have them in context
showish = any(w in q_low for w in ["show", "display", "print", "final contents", "list"])
mentions_logs = ("log file" in q_low) or ("*.log" in q_low) or ("log files" in q_low) or ("glob" in ctx.lower())
if showish or mentions_logs or "contents" in q_low:
# If we have a GLOB summary, format it a bit
if "GLOB " in ctx:
# Keep header + up to 50 matches
parts = ctx.splitlines()
header = next((p for p in parts if p.startswith("GLOB ")), None)
count = next((p for p in parts if p.startswith("COUNT:")), None)
matches = [p for p in parts if p and not p.startswith(("GLOB ", "COUNT:"))]
body = "\n".join(matches[:50]) if matches else "(no matches)"
text = "\n".join([x for x in [header, count, body] if x])
res_obj = {"type": "respond", "text": text, "ok": True}
results.append(res_obj); continue
# Otherwise just return the captured context (file contents, exist checks, shell output, etc.)
if ctx.strip():
res_obj = {"type": "respond", "text": ctx.strip(), "ok": True}
results.append(res_obj); continue
def _looks_like_echo(ans: str, q: str) -> bool:
a = _re.sub(r"\s+", " ", (ans or "").lower()).strip().rstrip("?.!")
qq = _re.sub(r"\s+", " ", (q or "").lower()).strip().rstrip("?.!")
# treat short or identical answers as echoes
return (not a) or (a == qq) or a.startswith(("answer clearly", "question:", "instruction:"))
# Retry once if echo-ish
if _looks_like_echo(answer, question):
retry_user = f"{question}\n\nRespond in 2–4 sentences. Do NOT repeat or quote the question."
answer = llm_generate_text(
model,
sys_prompt,
retry_user,
max_new_tokens=300
).strip()
# Guardrail 1: if the model claims "Insufficient data" but we DO have context, show the context/summarize
if ctx and "insufficient data" in (answer or "").lower():
# Prefer showing structured ctx (e.g., GLOB header) or first 25 lines
lines = [ln for ln in ctx.splitlines() if ln.strip()]
answer = "\n".join(lines[:25]) if lines else ctx.strip()
# Final clean & fallback
answer = _strip_meta_lines(answer)
# Guardrail 2: if answer has almost no overlap with ctx, produce a deterministic summary
if ctx:
import re as _re
ctx_tokens = set(_re.findall(r"[A-Za-z0-9_.:/-]+", ctx.lower()))
ans_tokens = set(_re.findall(r"[A-Za-z0-9_.:/-]+", (answer or "").lower()))
if len(ctx_tokens & ans_tokens) < 3:
# small, factual summary of context only
answer = llm_generate_text_exact(
model,
"Summarize ONLY the provided text into 2–4 short sentences. No new facts.",
ctx,
max_new_tokens=160
).strip()
# Only fall back if we somehow still have no answer
if not answer.strip():
prev_text = collect_text_context(results)
answer = (prev_text[:800].strip() if prev_text else "Sorry — I couldn’t produce an answer.")
res_obj = {"type": "respond", "text": answer, "ok": True}
elif t == "respond":
# Normal respond — but upgrade placeholders to real answers using previous context.
text = step.get("text", "")
placeholdery = (not text.strip()) or (text.strip().lower() in {"acknowledged.", "ok.", "okay.", "acknowledged"}) or ("<insert" in text.lower())
if placeholdery:
prev_text = collect_text_context(results)
if prev_text:
sys_prompt = (
"You convert raw outputs into a concise, friendly explanation. "
"Summarize what's most important for the user in a few sentences or bullets."
)
# Try to infer intent from the placeholder; default to 'summarize'
instruction = step.get("instruction", "Summarize the provided content.")
user_prompt = f"{instruction}\n\n---\n{prev_text}\n---"
text = llm_generate_text(model, sys_prompt, user_prompt, max_new_tokens=600).strip()
res_obj = {"type": "respond", "text": text, "ok": True}
elif t == "mkdirs":
made = []
for d in step.get("paths", []):
if not d: continue
os.makedirs(d, exist_ok=True)
made.append(d)
res_obj = {"type":"mkdirs","created":made,"ok":True}
elif t == "rewrite_file":
path = step["path"]
instruction = step.get("instruction", "")
length = step.get("length", "long")
size_hint = {"short":400,"medium":1200,"long":2400,"xl":4800}.get(length, 2400)
try:
with open(path, "r", errors="ignore") as f:
current = f.read()
except FileNotFoundError:
current = ""
sys_prompt = (
"You are editing a single file. Return ONLY the full, final file content. "
"No explanations, no backticks."
)
user_prompt = (
f"Instruction:\n{instruction}\n\n"
f"--- CURRENT FILE CONTENT START ---\n{current}\n--- CURRENT FILE CONTENT END ---"
)
new_content = llm_generate_text(model, sys_prompt, user_prompt, max_new_tokens=size_hint)
new_content = new_content.strip().removeprefix("```").removesuffix("```").strip()
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
f.write(new_content)
res_obj = {"type":"rewrite_file","path":path,"bytes":len(new_content.encode('utf-8')),"ok":True}
elif t == "fs":
op = (step.get("op") or "").lower()
path = step.get("path")
# safety: normalize/expand
if path:
path = os.path.expanduser(path)
if op == "list":
entries = sorted(os.listdir(path))
res_obj = {"type": "fs", "op": op, "path": path, "entries": entries, "count": len(entries), "ok": True}
elif op == "read":
with open(path, "r", errors="ignore") as f:
content = f.read()
res_obj = {"type": "fs", "op": op, "path": path, "content": content, "bytes": len(content.encode()), "ok": True}
elif op == "write":
content = step.get("content", "")
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
with open(path, "w", encoding="utf-8") as f:
f.write(content)
res_obj = {"type": "fs", "op": op, "path": path, "bytes": len(content.encode()), "ok": True}
elif op == "append":
content = step.get("content", "")
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
with open(path, "a", encoding="utf-8") as f:
f.write(content)
res_obj = {"type": "fs", "op": op, "path": path, "bytes": len(content.encode()), "ok": True}
elif op == "mkdir":
os.makedirs(path, exist_ok=True)
res_obj = {"type": "fs", "op": op, "path": path, "ok": True}
elif op == "remove":
# safe-ish delete: file or empty dir (no recursive by default)
if os.path.isdir(path):
os.rmdir(path) # raises if not empty (good)
else:
os.remove(path)
res_obj = {"type": "fs", "op": op, "path": path, "ok": True}
elif op == "move":
to = os.path.expanduser(step["to"])
os.makedirs(os.path.dirname(to) or ".", exist_ok=True)
os.replace(path, to)
res_obj = {"type": "fs", "op": op, "path": path, "to": to, "ok": True}
elif op == "copy":
to = os.path.expanduser(step["to"])
os.makedirs(os.path.dirname(to) or ".", exist_ok=True)
shutil.copy2(path, to)
res_obj = {"type": "fs", "op": op, "path": path, "to": to, "ok": True}
elif op == "exists":
res_obj = {"type": "fs", "op": op, "path": path, "exists": os.path.exists(path), "ok": True}
elif op == "glob":
import glob
patt = step.get("pattern") or path
if path and step.get("pattern"):
base = os.path.expanduser(path)
patt = os.path.join(base, step["pattern"])
matches = sorted(glob.glob(os.path.expanduser(patt)))
res_obj = {"type": "fs", "op": op, "pattern": patt, "matches": matches, "count": len(matches), "ok": True}
else:
res_obj = {"type": "error", "error": f"Unknown fs op '{op}'", "ok": False}
elif t == "generate_tree":
base = step.get("base") or "."
files = step.get("files") or []
os.makedirs(base, exist_ok=True)
written = []
for f in files:
rel = f.get("path")
if not rel: continue
path = os.path.join(base, rel)
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
fmt = f.get("format","text")
instr = f.get("instruction","")
length= f.get("length","medium")
# hint model with language if provided as code:<lang>
lang_hint = ""
if fmt.startswith("code:"):
lang_hint = f"\nLanguage: {fmt.split(':',1)[1]}"
fmt = "text"
# Literal content? (most of our scaffolds pass the final body)
if _looks_like_literal_content(path, fmt, instr) or os.path.basename(path).lower() == "requirements.txt":
content = instr
else:
sys_prompt = "Return ONLY the exact file content asked for. No explanations, no code fences, no headers."
size_hint = {"short":400, "medium":1200, "long":2400, "xl":4800}.get(length, 1200)
user_prompt = f"Format: {fmt}{lang_hint}\nInstruction: {instr}\n"
content = llm_generate_text_exact(model, sys_prompt, user_prompt, max_new_tokens=size_hint)
content = _sanitize_generated_content(path, fmt, content)
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
with open(path,"w",encoding="utf-8") as fp: fp.write(content)
written.append({"path":path,"bytes":len(content.encode('utf-8'))})
res_obj = {"type":"generate_tree","base":base,"written":written,"ok":True}
elif t == "generate_large_file":
path = step["path"]
chunks = step.get("chunks") or []
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
total = 0
with open(path,"w",encoding="utf-8") as fp:
for i, ck in enumerate(chunks, 1):
instr = ck.get("instruction","")
length = ck.get("length","medium")
size_hint = {"short":400, "medium":1200, "long":2400, "xl":4800}.get(length, 1200)
sys_prompt = (
"You are writing a specific section of a larger file. "
"Write only the requested section. No preambles, no backticks, no repetition."
)
user_prompt = f"Section {i}/{len(chunks)}:\n{instr}"
piece = llm_generate_text(model, sys_prompt, user_prompt, max_new_tokens=size_hint)
piece = piece.strip().removeprefix("```").removesuffix("```").strip()
fp.write(piece + ("\n" if not piece.endswith("\n") else ""))
total += len(piece.encode("utf-8"))
res_obj = {"type":"generate_large_file","path":path,"bytes":total,"chunks":len(chunks),"ok":True}
elif t == "shell":
# Resolve command by OS first
cmd = resolve_cmd_by_os(step["cmd"])
cwd = step.get("cwd") or None
timeout = float(step.get("timeout", 120))
env = os.environ.copy()
env.update(step.get("env", {}))
# Gather required tools: explicit + heuristic
requires = resolve_requires_by_os(step.get("requires") or step.get("needs"))
if not requires:
requires = _guess_tools_from_cmd(cmd)
missing = [tool for tool in requires if not _which(tool)]
preinstall_log = ""
if missing:
if ALLOW_AUTO_INSTALL:
ok_install, log, _installed = _install_missing_tools(missing)
preinstall_log = log
if not ok_install:
res_obj = {
"type": "shell",
"cmd": cmd,
"cwd": cwd,
"stdout": "",
"stderr": (
"Missing tools: " + ", ".join(missing) +
"\nAuto-install failed or not supported.\n" + log +
"\nTry manually: " + _suggest_install_cmd(missing)
),
"returncode": 127,
"ok": False,
"preinstall": preinstall_log,
}
results.append(res_obj)
continue
else:
res_obj = {
"type": "shell",
"cmd": cmd,
"cwd": cwd,
"stdout": "",
"stderr": (
"Missing tools: " + ", ".join(missing) +
"\nAuto-install disabled (set ALLOW_AUTO_INSTALL=1 on the server to enable for Linux)." +
"\nTry: " + _suggest_install_cmd(missing)
),
"returncode": 127,
"ok": False,
}
results.append(res_obj)
continue
# Run the intended command
proc = subprocess.run(
cmd, shell=True, capture_output=True, text=True,
cwd=cwd, timeout=timeout, env=env,
)
res_obj = {
"type": "shell",
"cmd": cmd,
"cwd": cwd,
"stdout": proc.stdout,
"stderr": proc.stderr,
"returncode": proc.returncode,
"ok": (proc.returncode == 0),
}
if preinstall_log:
res_obj["preinstall"] = preinstall_log
elif t == "generate_file":
path = step["path"]
instruction = step.get("instruction", "")
fmt = step.get("format", "text")
length = step.get("length", "medium")
lang_hint = ""
if isinstance(fmt, str) and fmt.startswith("code:"):
lang_hint = f"\nLanguage: {fmt.split(':',1)[1]}"
fmt = "text"
if _looks_like_literal_content(path, fmt, instruction) or os.path.basename(path).lower() == "requirements.txt":
content = instruction
else:
sys_prompt = "Return ONLY the exact file content asked for. No explanations, no code fences, no headers."
size_hint = {"short":400,"medium":1200,"long":2400}.get(length,1200)
user_prompt = f"Format: {fmt}{lang_hint}\nInstruction: {instruction}\n"
content = llm_generate_text_exact(model, sys_prompt, user_prompt, max_new_tokens=size_hint)
content = _sanitize_generated_content(path, fmt, content)
os.makedirs(os.path.dirname(path), exist_ok=True)
with open(path, "w") as f:
f.write(content)
res_obj = {"type": "generate_file", "path": path, "status": "ok", "bytes": len(content.encode('utf-8')), "ok": True}
elif t == "read_file":
path = step["path"]
with open(path, "r", errors="ignore") as f:
content = f.read()
res_obj = {"type": "read_file", "path": path, "content": content, "bytes": len(content.encode("utf-8")), "line_count": (content.count("\n")+1 if content else 0), "ok": True}
elif t in ("write_file", "edit_file", "append_file"):
path = step["path"]
content = step.get("content", "")
mode = "w" if t != "append_file" else "a"
os.makedirs(os.path.dirname(path) or ".", exist_ok=True)
with open(path, mode) as f:
f.write(content)
res_obj = {"type": t, "path": path, "mode": mode, "status": "ok", "bytes": len(content.encode("utf-8")), "line_count": (content.count("\n")+1 if content else 0), "ok": True}
elif t == "list_dir":
path = step.get("path", ".")
entries = sorted(os.listdir(path))
res_obj = {"type": "list_dir", "path": path, "entries": entries, "count": len(entries), "ok": True}
elif t == "python":
code = step["code"]
output = safe_exec_python(code)
ok_flag = ("Traceback (most recent call last):" not in output)
res_obj = {"type": "python", "stdout": output, "ok": ok_flag}
else:
res_obj = {"type": "error", "error": f"Unknown step type {t}", "ok": False}
except Exception as e:
res_obj = {"type": "error", "error": str(e), "trace": traceback.format_exc(), "step": step, "index": idx, "ok": False}
res_obj["duration_ms"] = int((time.time() - started) * 1000)
results.append(res_obj)
return jsonify({"results": results})
# server (new endpoint)
@app.route("/assist/rewrite", methods=["POST"])
@require_api_key
def assist_rewrite():
j = request.json or {}
instruction = j.get("instruction","")
current = j.get("current","")
length = j.get("length","long")
sys = "You are editing a single file. Return ONLY the full, final file content. No backticks."
user = f"Instruction:\n{instruction}\n\n--- CURRENT ---\n{current}\n--- END ---"
out = llm_generate_text_exact(model, sys, user, max_new_tokens={"short":400,"medium":1200,"long":2400,"xl":4800}[length])
return jsonify({"new_content": _sanitize_generated_content(None, "text", out)})
@app.get("/")
def root():
return send_from_directory(app.static_folder, "index.html")
# (optional convenience)
@app.get("/index.html")
def root_html():
return send_from_directory(app.static_folder, "index.html")
# keep status JSON as-is
@app.get("/status")
def status():
return jsonify(ok=True, model=MODEL_NAME)
# ──────────────────────────────────────────────
# 4) Main
# ──────────────────────────────────────────────
if __name__ == "__main__":
with app.app_context():
init_db()
port = int(os.environ.get("PORT", 5005))
print(f"[+] Llama3-Agent server running on http://0.0.0.0:{port}")
app.run(host="0.0.0.0", port=port, debug=False)
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