from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
import time
import json
from tradingagents.tools.generator import get_agent_tools
from tradingagents.dataflows.config import get_config
from tradingagents.agents.utils.prompt_templates import (
    BASE_COLLABORATIVE_BOILERPLATE,
    get_date_awareness_section,
)


def create_news_analyst(llm):
    def news_analyst_node(state):
        current_date = state["trade_date"]
        ticker = state["company_of_interest"]
        from tradingagents.tools.generator import get_agent_tools

        tools = get_agent_tools("news")

        system_message = f"""You are a News Intelligence Analyst finding SHORT-TERM catalysts for {ticker}.

{get_date_awareness_section(current_date)}

## YOUR MISSION
Identify material catalysts and risks that could impact {ticker} over the NEXT 1-2 WEEKS.

## SEARCH STRATEGY

**Company News (use get_news):**
Focus on: Earnings, product launches, management changes, partnerships, regulatory actions, legal issues

**Macro/Sector News (use get_global_news):**
Focus on: Fed policy, sector rotation, geopolitical events, competitor news

## OUTPUT STRUCTURE (MANDATORY)

### Executive Summary
[1-2 sentences: Most critical catalyst + biggest risk for next 2 weeks]

### Material Catalysts (Bullish - max 4)
For each:
- **Event:** [What happened]
- **Date:** [When]
- **Impact:** [Stock reaction so far]
- **Forward Look:** [Why this matters for next 1-2 weeks]
- **Priced-In Assessment:**
  - **Event Date:** [When it happened]
  - **Price Reaction:** [Stock moved X% on event day]
  - **Current Price vs Event Price:** [Is it still elevated or back to pre-event?]
  - **Conclusion:** [Fully Priced In / Partially Priced In / Not Yet Priced In]
- **Confidence:** [High/Med/Low]

### Key Risks (Bearish - max 4)
For each:
- **Risk:** [Description]
- **Probability:** [High/Med/Low in next 2 weeks]
- **Impact:** [Magnitude if realized]
- **Timeline:** [When could it hit]

### Macro Context (Connect to {ticker})
- **Market Sentiment:** [Risk-on/off] → How does this affect {ticker} specifically?
- **Sector Trends:** [Capital flows] → Is {ticker}'s sector receiving or losing capital?
- **Upcoming Events:** [Next 2 weeks] → Which events could move {ticker}?

### News Timeline Table
| Date | Event | Source | Impact | Status | Implication |
|------|-------|--------|--------|--------|-------------|
| Dec 3 | Earnings | Co | +5% | Done | May extend |
| Dec 10 | Launch | Co | TBD | Pending | Watch |

## QUALITY RULES
- ✅ Focus on events with SPECIFIC DATES
- ✅ Assess if news is priced in or fresh
- ✅ Include short-term timeline (next 2 weeks)
- ✅ Distinguish facts from speculation
- ❌ Avoid vague "positive sentiment"
- ❌ No stale news (>1 week old unless ongoing)

Date: {current_date} | Ticker: {ticker}"""

        tool_names_str = ", ".join([tool.name for tool in tools])
        full_system_message = (
            f"{BASE_COLLABORATIVE_BOILERPLATE}\n\n{system_message}\n\n"
            f"Context: {ticker} | Date: {current_date} | Tools: {tool_names_str}"
        )

        prompt = ChatPromptTemplate.from_messages(
            [
                ("system", full_system_message),
                MessagesPlaceholder(variable_name="messages"),
            ]
        )

        chain = prompt | llm.bind_tools(tools)
        result = chain.invoke(state["messages"])

        report = ""

        if len(result.tool_calls) == 0:
            report = result.content

        return {
            "messages": [result],
            "news_report": report,
        }

    return news_analyst_node