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Update app.py
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app.py
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# app.py
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#
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from typing import List, Tuple
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try:
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return importlib.import_module(pkg)
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except ModuleNotFoundError:
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target = f"{pkg}=={
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print(f"[bootstrap]
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subprocess.check_call([sys.executable, "-m", "pip", "install", target])
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return importlib.import_module(pkg)
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#
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pytz
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langchain
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# 3. Tool: current time in timezone
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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"""Return the current local time
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try:
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tz = pytz.timezone(timezone)
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return datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
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except Exception as e:
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return f"Error: {e}"
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workday_end: int = 18,
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slot_minutes: int = 30
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) -> str:
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"""
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Find the next common slot across multiple time-zones.
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Args:
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timezones: list of IANA tz strings
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workday_start / workday_end: local work hours (inclusive start, exclusive end)
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slot_minutes: length of slot
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"""
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now_utc = datetime.datetime.utcnow().replace(tzinfo=pytz.utc)
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intervals: List[Tuple[datetime.datetime, datetime.datetime]] = []
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for tz_name in timezones:
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try:
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tz = pytz.timezone(tz_name)
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except pytz.UnknownTimeZoneError:
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return f"Unknown timezone: {tz_name}"
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local_now = now_utc.astimezone(tz)
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start_local = local_now.replace(hour=workday_start, minute=0, second=0, microsecond=0)
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end_local = local_now.replace(hour=workday_end, minute=0, second=0, microsecond=0)
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# move window to tomorrow if current time past work hours
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if local_now >= end_local:
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start_local += datetime.timedelta(days=1)
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end_local += datetime.timedelta(days=1)
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elif local_now > start_local:
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start_local = local_now # cannot schedule in the past
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intervals.append((start_local.astimezone(pytz.utc), end_local.astimezone(pytz.utc)))
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slot_start = max(iv[0] for iv in intervals)
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slot_end = min(iv[1] for iv in intervals)
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if slot_end - slot_start < datetime.timedelta(minutes=slot_minutes):
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return "No overlapping work-hour slot found in the next day."
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chosen_end = slot_start + datetime.timedelta(minutes=slot_minutes)
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lines = [
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f"Proposed {slot_minutes}-minute stand-up:",
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f"• UTC: {slot_start.strftime('%Y-%m-%d %H:%M')} – {chosen_end.strftime('%H:%M')}"
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]
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for tz_name in timezones:
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tz = pytz.timezone(tz_name)
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local_start = slot_start.astimezone(tz).strftime('%Y-%m-%d %H:%M')
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local_end = chosen_end.astimezone(tz).strftime('%H:%M')
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lines.append(f"• {tz_name}: {local_start} – {local_end}")
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return "\n".join(lines)
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# 5. Build LangChain tools list
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tools = [
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Tool.from_function(
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func=find_overlap_slot,
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name="find_overlap_slot",
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description=(
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"Find a meeting slot. Args: timezones (List[str]), "
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"workday_start, workday_end, slot_minutes."
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),
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),
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Tool.from_function(
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func=get_current_time_in_timezone,
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name="get_current_time_in_timezone",
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description="Return current local time for a timezone.",
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),
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]
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#
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agent = create_openai_functions_agent(llm, tools)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=False)
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# 7. Minimal Gradio interface
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import gradio as gr
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def chat_agent(user_input, history):
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"""Wrapper to make the agent compatible with Gradio ChatInterface."""
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result = agent_executor.invoke({"input": user_input})
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return result["output"]
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gr.Markdown("# 🕒 Time-zone Helper Agent")
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gr.ChatInterface(chat_agent)
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# 8. Launch if running locally; HF Spaces ignores this in production
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if __name__ == "__main__":
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# ───────────────────────────── app.py ─────────────────────────────────────
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# NOTE: just push this one file to your HF Space repo. The bootstrap section
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# installs the needed packages inside the container at first launch.
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# ╭──────────────────────── 0. Bootstrap helper ─────────────────────────────╮
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import importlib, subprocess, sys, os, datetime
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from typing import List
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def ensure(pkg: str, ver: str | None = None):
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"""Import a package or pip-install it into the current env, then import."""
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try:
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return importlib.import_module(pkg)
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except ModuleNotFoundError:
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target = f"{pkg}=={ver}" if ver else pkg
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print(f"[bootstrap] installing {target} …", flush=True)
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subprocess.check_call([sys.executable, "-m", "pip", "install", target])
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return importlib.import_module(pkg)
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# minimal deps: pytz + *monolithic* langchain (0.1.x) + openai
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pytz = ensure("pytz")
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langchain = ensure("langchain", "0.1.16") # stable, includes tool decorator
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openai_pkg = ensure("openai") # backend used by ChatOpenAI
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# ╰───────────────────────────────────────────────────────────────────────────╯
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# ╭──────────────────────── 1. Imports after install ────────────────────────╮
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from langchain.tools import tool, Tool
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from langchain.chat_models import ChatOpenAI
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from langchain.agents import create_openai_functions_agent, AgentExecutor
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# ╰───────────────────────────────────────────────────────────────────────────╯
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# ╭──────────────────────── 2. Tool definitions ─────────────────────────────╮
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@tool
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def my_custom_tool(arg1: str, arg2: int) -> str:
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"""A placeholder tool you can extend later.
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Args:
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arg1: any string
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arg2: any integer
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"""
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return f"Received arg1='{arg1}', arg2={arg2}. Build your magic here!"
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@tool
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def get_current_time_in_timezone(timezone: str) -> str:
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"""Return the current local time in the given IANA timezone."""
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try:
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tz = pytz.timezone(timezone)
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return datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
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except Exception as e:
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return f"Error: {e}"
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# ╰───────────────────────────────────────────────────────────────────────────╯
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# ╭──────────────────────── 3. Build agent once ─────────────────────────────╮
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tools: List[Tool] = [
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Tool.from_function(my_custom_tool, name="my_custom_tool"),
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Tool.from_function(get_current_time_in_timezone, name="get_current_time_in_timezone"),
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]
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llm = ChatOpenAI( # uses env var OPENAI_API_KEY
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model = "gpt-3.5-turbo",
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temperature = 0,
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)
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agent = create_openai_functions_agent(llm, tools)
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agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=False)
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# ╰───────────────────────────────────────────────────────────────────────────╯
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# ╭──────────────────────── 4. CLI test loop (optional) ─────────────────────╮
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if __name__ == "__main__":
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if not os.getenv("OPENAI_API_KEY"):
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print("⚠️ Please set OPENAI_API_KEY as an env-var (HF Space → Secrets).")
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print("🔮 Agent ready. Type a question or 'q' to quit.")
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while True:
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user = input("🗣 ")
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if user.lower().strip() in {"q", "quit", "exit"}:
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break
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result = agent_executor.invoke({"input": user})
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print("🤖", result["output"])
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# ╰───────────────────────────────────────────────────────────────────────────╯
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