app.py

# app.py
import os, asyncio, aiohttp, nest_asyncio
from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
from llama_index.tools.weather import OpenWeatherMapToolSpec
from llama_index.tools.playwright import PlaywrightToolSpec
from llama_index.core.tools import FunctionTool
from llama_index.core.agent.workflow import ReActAgent, FunctionAgent, AgentWorkflow
from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
from llama_index.llms.openai import OpenAI
from llama_index.core.memory import ChatMemoryBuffer
from llama_index.readers.web import RssReader
from llama_index.core.workflow import Context
import gradio as gr
import subprocess
subprocess.run(["playwright", "install"])

# allow nested loops in Spaces
nest_asyncio.apply()

# --- Secrets via env vars ---
HF_TOKEN            = os.getenv("HF_TOKEN")
OPENAI_API_KEY      = os.getenv("OPENAI_API_KEY")
OPENWEATHERMAP_KEY  = os.getenv("OPENWEATHERMAP_API_KEY")
SERPER_API_KEY      = os.getenv("SERPER_API_KEY")

# --- LLMs ---
hf_llm = HuggingFaceInferenceAPI(
    model_name="Qwen/Qwen2.5-Coder-32B-Instruct",
    token=HF_TOKEN, 
    task="conversational"
)
# OpenAI for pure function-calling
openai_llm = OpenAI(
    model="gpt-4o",
    api_key=userdata.get("OPENAI_API_KEY"),
    temperature=0.0,
    streaming=False,
)

# --- Memory ---
memory = ChatMemoryBuffer.from_defaults(token_limit=4096)

# --- Tools Setup ---
# DuckDuckGo
duck_spec = DuckDuckGoSearchToolSpec()
search_tool = FunctionTool.from_defaults(duck_spec.duckduckgo_full_search)

# Weather
weather_spec = OpenWeatherMapToolSpec(key=OPENWEATHERMAP_KEY)
weather_cur = FunctionTool.from_defaults(
    weather_spec.weather_at_location,
    name="current_weather",
    description="Get the current weather for a location."
)
weather_fc = FunctionTool.from_defaults(
    weather_spec.forecast_tommorrow_at_location,
    name="weather_forecast",
    description="Get tomorrow’s forecast for a location."
)

# Playwright (synchronous start)
async def _start_browser():
    return await PlaywrightToolSpec.create_async_playwright_browser(headless=True)
browser = asyncio.get_event_loop().run_until_complete(_start_browser())
pw_spec = PlaywrightToolSpec.from_async_browser(browser)
navigate_tool = FunctionTool.from_defaults(pw_spec.navigate_to,    name="web_navigate",   description="Go to URL")
extract_text = FunctionTool.from_defaults(pw_spec.extract_text,     name="web_extract_text", description="Extract page text")
# …add extract_links if desired…

# Google News RSS
def fetch_google_news_rss():
    docs = RssReader(html_to_text=True).load_data(["https://news.google.com/rss"])
    return [{"title":d.metadata.get("title",""), "url":d.metadata.get("link","")} for d in docs]
google_rss_tool = FunctionTool.from_defaults(
    fn=fetch_google_news_rss,
    name="fetch_google_news_rss",
    description="Get headlines & URLs from Google News RSS."
)

# Serper
async def fetch_serper(ctx, query):
    if not SERPER_API_KEY:
        raise ValueError("SERPER_API_KEY missing")
    url = f"https://google.serper.dev/news?q={query}&tbs=qdr%3Ad"
    hdr = {"X-API-KEY": SERPER_API_KEY, "Content-Type":"application/json"}
    async with aiohttp.ClientSession() as s:
        r = await s.get(url, headers=hdr)
        r.raise_for_status()
        return await r.json()
serper_tool = FunctionTool.from_defaults(
    fetch_serper, name="fetch_news_from_serper", 
    description="Search today’s news via Serper."
)

# --- Agents ---
# 1. Google News RSS Agent (replaces old google_news_agent)
google_rss_agent = FunctionAgent(
    name="google_rss_agent",
    description="Fetches latest headlines and URLs from Google News RSS feed.",
    system_prompt="You are an agent that fetches the latest headlines and URLs from the Google News RSS feed.",
    tools=[google_rss_tool],
    llm=openai_llm,
    memory=memory,
)

# 2. Web Browsing Agent
web_browsing_agent = ReActAgent(
    name="web_browsing_agent",
    description="Fetches Serper URLs, navigates to each link, extracts the text and builds a summary",
    system_prompt=(
        "You are a news-content agent. When asked for details on a headline:\n"
        "1. Call `fetch_news_from_serper(query)` to get JSON with article URLs.\n"
        "2. For each top URL, call `web_navigate(url)` then `web_extract_text()`.\n"
        "3. Synthesize those texts into a concise summary."
    ),
    tools=[serper_news_tool, navigate_tool, extract_text_tool, extract_links_tool],
    llm=llm,
    memory=memory,
)

# 3. Weather Agent
weather_agent = ReActAgent(
    name="weather_agent",
    description="Answers weather-related questions.",
    system_prompt="You are a weather agent that provides current weather and forecasts.",
    tools=[weather_tool, forecast_tool],
    llm=openai_llm,
)

# 4. DuckDuckGo Search Agent
search_agent = ReActAgent(
    name="search_agent",
    description="Searches general info using DuckDuckGo.",
    system_prompt="You are a search agent that uses DuckDuckGo to answer questions.",
    tools=[search_tool],
    llm=openai_llm,
)

router_agent = ReActAgent(
    name="router_agent",
    description="Routes queries to the correct specialist agent.",
    system_prompt=(
        "You are RouterAgent. "
        "Given the user query, reply with exactly one name from: "
        "['google_rss_agent','weather_agent','search_agent','web_browsing_agent']."
    ),
    llm=llm,
    tools=[
        FunctionTool.from_defaults(
            fn=lambda ctx, choice: choice,
            name="choose_agent",
            description="Return the chosen agent name."
        )
    ],
    can_handoff_to=[
        "google_rss_agent",
        "weather_agent",
        "search_agent",
        "web_browsing_agent",
    ],
)

workflow = AgentWorkflow(
    agents=[router, google_rss_agent, web_agent, weather_agent, search_agent],
    root_agent="router_agent"
)
ctx = Context(workflow)

# Sync wrapper
def respond(query: str) -> str:
    out = asyncio.run(workflow.run(user_msg=query, ctx=ctx, memory=memory))
    return out.response.blocks[0].text

# --- Gradio UI ---
with gr.Blocks() as demo:
    gr.Markdown("## 🗞️ Multi‐Agent News & Weather Chatbot")
    chatbot = gr.Chatbot()
    txt     = gr.Textbox(placeholder="Ask me about news, weather or anything…")
    txt.submit(lambda q, chat: (chat + [[q, respond(q)]]), [txt, chatbot], chatbot)
    demo.launch()