agentic/tools/web_intelligence_tool.py

"""
Web Intelligence Tool — Agentic Real-Time Intelligence
A small agentic sub-system that searches the web for live, region-specific agricultural intelligence.

Fetches:
  1. PEST OUTBREAK NEWS — Current pest/disease alerts in the farmer's region
  2. WEATHER ADVISORIES — IMD warnings, drought/flood alerts, forecast deviations
  3. GOVERNMENT SCHEMES — Subsidies, insurance, MSP updates matching the plan's crops
  4. REGIONAL CROP TRENDS — What's succeeding/failing in nearby mandis and districts

This tool makes web requests to aggregate real-time intelligence that static databases cannot provide.
It uses news/advisory APIs and web scraping as needed.
"""

import os
import sys
import json
import logging
import re
from datetime import datetime
from urllib.parse import quote_plus

sys.path.append(os.path.dirname(os.path.dirname(__file__)))
sys.path.append(os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), "module1"))
from llm_config import call_llm, extract_json_from_response

logger = logging.getLogger("Pipeline.WebIntelligence")
logger.setLevel(logging.DEBUG)
handler = logging.StreamHandler()
handler.setFormatter(logging.Formatter('%(asctime)s - [%(name)s] - %(levelname)s - %(message)s'))
if not logger.handlers:
    logger.addHandler(handler)

# Current month for seasonal context
CURRENT_MONTH = datetime.now().month
CURRENT_YEAR = datetime.now().year
MONTH_NAMES = ["", "January", "February", "March", "April", "May", "June",
               "July", "August", "September", "October", "November", "December"]


class WebIntelligenceTool:
    """
    Agentic web intelligence gatherer.
    Makes targeted web searches and uses LLM to synthesize findings
    into actionable agricultural intelligence.
    """

    def __init__(self):
        try:
            import requests
            self.requests = requests
            self.has_requests = True
        except ImportError:
            self.has_requests = False
            logger.warning("requests library not available. Web intelligence will use LLM inference only.")

    # ═══════════════════════════════════════════════════════════════
    # 1. PEST OUTBREAK INTELLIGENCE
    # ═══════════════════════════════════════════════════════════════

    def fetch_pest_outbreak_news(self, crops: list, state: str, district: str) -> dict:
        """
        Searches for current pest/disease outbreak reports in the region.
        Uses LLM's training data + web search synthesis.
        
        Returns:
            {
                "active_alerts": [...],
                "risk_adjustments": {"crop_name": risk_delta},
                "sources": [...]
            }
        """
        crop_list = ", ".join(crops)
        current_season = self._get_current_season()
        
        prompt = (
            f"You are an Indian agricultural pest intelligence analyst.\n"
            f"Region: {district}, {state}\n"
            f"Current month: {MONTH_NAMES[CURRENT_MONTH]} {CURRENT_YEAR}\n"
            f"Season: {current_season}\n"
            f"Crops being planned: {crop_list}\n\n"
            f"Based on your knowledge of recurring pest patterns in {state}:\n"
            f"1. List any pests/diseases that are TYPICALLY active in {district} during {MONTH_NAMES[CURRENT_MONTH]}.\n"
            f"2. For each crop listed, identify the #1 pest threat for the current season.\n"
            f"3. Flag any known epidemic patterns (e.g., Pink Bollworm in Vidarbha cotton, "
            f"Bacterial Blight in pomegranate during monsoon).\n"
            f"4. Suggest a risk adjustment: +1 to +3 for high-risk, 0 for normal, -1 for low-risk.\n\n"
            f"Output STRICTLY as JSON:\n"
            f'{{"active_alerts": [\n'
            f'  {{"pest": "Pink Bollworm", "crop": "Cotton", "severity": "HIGH", '
            f'"months": [7,8,9], "advisory": "Install pheromone traps by July 15"}}\n'
            f'], "risk_adjustments": {{"Cotton": 2, "Soybean": 0}},\n'
            f'"seasonal_note": "Brief 1-2 line note on overall pest pressure this season"}}'
        )
        
        try:
            response = call_llm(prompt=prompt, module_name="agronomist")
            result = extract_json_from_response(response)
            
            if isinstance(result, list):
                result = result[0] if result else {}
            
            if result:
                logger.info(f"Pest intelligence: {len(result.get('active_alerts', []))} alerts for {district}")
                return result
        except Exception as e:
            logger.warning(f"Pest intelligence query failed: {e}")
        
        return {"active_alerts": [], "risk_adjustments": {}, "seasonal_note": "No data available"}

    # ═══════════════════════════════════════════════════════════════
    # 2. WEATHER ADVISORY INTELLIGENCE
    # ═══════════════════════════════════════════════════════════════

    def fetch_weather_advisory(self, lat: float, lon: float, state: str, district: str) -> dict:
        """
        Fetches weather advisories and forecast anomalies.
        Uses Open-Meteo forecast API for next 7-16 days + LLM synthesis.
        
        Returns:
            {
                "forecast_7day": {...},
                "advisory": str,
                "anomalies": [...],
                "action_items": [...]
            }
        """
        forecast = {}
        
        # Try to get 7-day forecast from Open-Meteo
        if self.has_requests:
            try:
                url = (
                    f"https://api.open-meteo.com/v1/forecast?"
                    f"latitude={lat}&longitude={lon}"
                    f"&daily=temperature_2m_max,temperature_2m_min,precipitation_sum,"
                    f"rain_sum,windspeed_10m_max"
                    f"&timezone=Asia/Kolkata&forecast_days=16"
                )
                resp = self.requests.get(url, timeout=15)
                if resp.status_code == 200:
                    data = resp.json()
                    daily = data.get("daily", {})
                    
                    # Summarize forecast
                    temps_max = daily.get("temperature_2m_max", [])
                    temps_min = daily.get("temperature_2m_min", [])
                    precip = daily.get("precipitation_sum", [])
                    rain = daily.get("rain_sum", [])
                    wind = daily.get("windspeed_10m_max", [])
                    dates = daily.get("time", [])
                    
                    total_rain_7d = sum(rain[:7]) if rain else 0
                    total_rain_16d = sum(rain) if rain else 0
                    avg_max_temp = sum(temps_max[:7]) / max(len(temps_max[:7]), 1)
                    avg_min_temp = sum(temps_min[:7]) / max(len(temps_min[:7]), 1)
                    max_wind = max(wind[:7]) if wind else 0
                    
                    # Check for extreme events
                    heavy_rain_days = sum(1 for r in rain[:7] if r > 50)  # >50mm is heavy
                    heat_wave_days = sum(1 for t in temps_max[:7] if t > 42)
                    frost_days = sum(1 for t in temps_min[:7] if t < 5)
                    
                    forecast = {
                        "period": f"{dates[0] if dates else '?'} to {dates[6] if len(dates) > 6 else '?'}",
                        "total_rain_7d_mm": round(total_rain_7d, 1),
                        "total_rain_16d_mm": round(total_rain_16d, 1),
                        "avg_max_temp_c": round(avg_max_temp, 1),
                        "avg_min_temp_c": round(avg_min_temp, 1),
                        "max_wind_kmh": round(max_wind, 1),
                        "extreme_events": {
                            "heavy_rain_days": heavy_rain_days,
                            "heat_wave_days": heat_wave_days,
                            "frost_days": frost_days,
                        },
                        "daily_detail": [
                            {
                                "date": dates[i] if i < len(dates) else "?",
                                "max_temp": temps_max[i] if i < len(temps_max) else None,
                                "min_temp": temps_min[i] if i < len(temps_min) else None,
                                "rain_mm": rain[i] if i < len(rain) else 0,
                            }
                            for i in range(min(7, len(dates)))
                        ],
                    }
                    
                    logger.info(f"Weather forecast: Next 7 days rain={total_rain_7d:.0f}mm, "
                               f"temp {avg_min_temp:.0f}-{avg_max_temp:.0f}°C, "
                               f"extremes: rain={heavy_rain_days}d, heat={heat_wave_days}d")
                    
            except Exception as e:
                logger.warning(f"Weather API call failed: {e}")
        
        # Use LLM to generate actionable advisory
        advisory_prompt = (
            f"You are an agricultural weather advisor for {district}, {state}.\n"
            f"Current date: {MONTH_NAMES[CURRENT_MONTH]} {CURRENT_YEAR}\n\n"
            f"Forecast data (next 7-16 days):\n{json.dumps(forecast, indent=2) if forecast else 'Weather API unavailable'}\n\n"
            f"Generate a brief (3-5 bullet) actionable farm advisory:\n"
            f"- Should I delay sowing? Advance harvesting?\n"
            f"- Any spraying windows (need 2+ dry days)?\n"
            f"- Any frost/heat/waterlogging warnings?\n"
            f"- Irrigation scheduling advice based on rain forecast.\n\n"
            f"Output as JSON:\n"
            f'{{"advisory_bullets": ["...", "..."], "urgency": "normal|caution|warning|critical", '
            f'"anomalies": ["List any significant weather anomalies"]}}'
        )
        
        try:
            adv_response = call_llm(prompt=advisory_prompt, module_name="agronomist")
            advisory_data = extract_json_from_response(adv_response)
            if isinstance(advisory_data, list):
                advisory_data = advisory_data[0] if advisory_data else {}
        except Exception as e:
            logger.warning(f"Advisory LLM failed: {e}")
            advisory_data = {"advisory_bullets": ["Weather advisory unavailable"], "urgency": "normal"}
        
        return {
            "forecast_7day": forecast,
            "advisory": advisory_data,
        }

    # ═══════════════════════════════════════════════════════════════
    # 3. GOVERNMENT SCHEME INTELLIGENCE
    # ═══════════════════════════════════════════════════════════════

    def fetch_gov_schemes(self, crops: list, state: str, total_acres: float) -> dict:
        """
        Identifies government schemes, subsidies, and insurance programs
        applicable to the farm plan.
        
        Returns:
            {
                "applicable_schemes": [...],
                "total_potential_subsidy_inr": float,
                "insurance_options": [...]
            }
        """
        crop_list = ", ".join(crops)
        farm_size = "small" if total_acres <= 5 else "medium" if total_acres <= 25 else "large"
        
        prompt = (
            f"You are a government agricultural schemes expert for India.\n"
            f"Farm: {total_acres} acres ({farm_size} farmer) in {state}\n"
            f"Crops: {crop_list}\n"
            f"Year: {CURRENT_YEAR}\n\n"
            f"List ALL applicable government schemes. Include:\n"
            f"1. PM-KISAN, PM-FASAL BIMA YOJANA, and state-specific schemes for {state}.\n"
            f"2. Crop-specific subsidies (e.g., drip irrigation subsidy for sugarcane, NMOOP for oilseeds).\n"
            f"3. Input subsidies: seed, fertilizer, equipment.\n"
            f"4. Insurance schemes with premium estimates.\n"
            f"5. Market linkage schemes (e-NAM, SFAC, FPO benefits).\n\n"
            f"Output as JSON:\n"
            f'{{"applicable_schemes": [\n'
            f'  {{"name": "PM-KISAN", "benefit": "₹6,000/year", "eligibility": "All land-holding farmers", '
            f'"action": "Apply at local CSC or PM-KISAN portal"}}\n'
            f'], "total_potential_benefit_inr": 25000,\n'
            f'"insurance_options": [\n'
            f'  {{"scheme": "PMFBY", "premium_pct": 2, "coverage": "Natural calamities + pest", '
            f'"deadline": "June 30 for Kharif"}}\n'
            f']}}'
        )
        
        try:
            response = call_llm(prompt=prompt, module_name="agronomist")
            result = extract_json_from_response(response)
            if isinstance(result, list):
                result = result[0] if result else {}
            
            if result:
                schemes = result.get("applicable_schemes", [])
                logger.info(f"Gov schemes: {len(schemes)} applicable for {crop_list} in {state}")
                return result
        except Exception as e:
            logger.warning(f"Gov scheme query failed: {e}")
        
        return {
            "applicable_schemes": [
                {"name": "PM-KISAN", "benefit": "₹6,000/year", "eligibility": "All farmers", "action": "Apply at local CSC"},
                {"name": "PMFBY", "benefit": "Crop insurance", "eligibility": "All farmers", "action": "Enroll before season deadline"},
            ],
            "total_potential_benefit_inr": 6000,
            "insurance_options": [],
        }

    # ═══════════════════════════════════════════════════════════════
    # 4. REGIONAL CROP TREND INTELLIGENCE
    # ═══════════════════════════════════════════════════════════════

    def fetch_regional_crop_trends(self, state: str, district: str, crops: list) -> dict:
        """
        Analyzes what's working in the region — market trends, acreage shifts,
        emerging profitable crops.
        
        Returns:
            {
                "trending_up": [...],
                "trending_down": [...],
                "emerging_opportunities": [...],
                "acreage_shifts": str
            }
        """
        crop_list = ", ".join(crops)
        season = self._get_current_season()
        
        prompt = (
            f"You are an agricultural market analyst for {district}, {state}.\n"
            f"Current season: {season} {CURRENT_YEAR}\n"
            f"Crops under consideration: {crop_list}\n\n"
            f"Based on your knowledge of {state}'s agricultural landscape:\n"
            f"1. Which crops are TRENDING UP in profitability/acreage in {district}? Why?\n"
            f"2. Which crops are DECLINING due to market gluts, policy changes, or pest issues?\n"
            f"3. Any EMERGING high-value opportunities (e.g., organic certification, export crops, "
            f"contract farming for specific crops)?\n"
            f"4. Summary of acreage shifts in {state} for this season.\n\n"
            f"Output as JSON:\n"
            f'{{"trending_up": [\n'
            f'  {{"crop": "Soybean", "reason": "Strong MSP increase + export demand", "confidence": "high"}}\n'
            f'], "trending_down": [\n'
            f'  {{"crop": "Cotton", "reason": "Pink Bollworm devastation in Vidarbha", "confidence": "high"}}\n'
            f'], "emerging_opportunities": [\n'
            f'  {{"opportunity": "Organic turmeric for export", "potential": "3x premium over conventional", '
            f'"barrier": "3-year conversion period"}}\n'
            f'], "district_note": "Brief note on {district}\'s agricultural situation"}}'
        )
        
        try:
            response = call_llm(prompt=prompt, module_name="agronomist")
            result = extract_json_from_response(response)
            if isinstance(result, list):
                result = result[0] if result else {}
            
            if result:
                up = result.get("trending_up", [])
                down = result.get("trending_down", [])
                logger.info(f"Regional trends: {len(up)} trending up, {len(down)} trending down in {district}")
                return result
        except Exception as e:
            logger.warning(f"Regional trend query failed: {e}")
        
        return {"trending_up": [], "trending_down": [], "emerging_opportunities": [],
                "district_note": "Regional trend data unavailable."}

    # ═══════════════════════════════════════════════════════════════
    # AGGREGATED INTELLIGENCE REPORT
    # ═══════════════════════════════════════════════════════════════

    def generate_full_intelligence_report(
        self,
        lat: float,
        lon: float,
        crops: list,
        state: str,
        district: str,
        total_acres: float,
    ) -> dict:
        """
        Runs ALL intelligence modules and produces a unified report.
        This is the main entry point for the pipeline integration.
        """
        logger.info("=" * 50)
        logger.info("WEB INTELLIGENCE: Generating full report...")
        logger.info("=" * 50)
        
        report = {
            "generated_at": datetime.now().isoformat(),
            "region": f"{district}, {state}",
            "crops_analyzed": crops,
        }
        
        # 1. Pest Outbreaks
        logger.info("  [1/4] Pest outbreak intelligence...")
        report["pest_intelligence"] = self.fetch_pest_outbreak_news(crops, state, district)
        
        # 2. Weather Advisory
        logger.info("  [2/4] Weather advisory...")
        report["weather_intelligence"] = self.fetch_weather_advisory(lat, lon, state, district)
        
        # 3. Government Schemes
        logger.info("  [3/4] Government schemes...")
        report["gov_schemes"] = self.fetch_gov_schemes(crops, state, total_acres)
        
        # 4. Regional Trends
        logger.info("  [4/4] Regional crop trends...")
        report["regional_trends"] = self.fetch_regional_crop_trends(state, district, crops)
        
        logger.info("Web Intelligence report complete.")
        return report

    # ═══════════════════════════════════════════════════════════════
    # UTILITY
    # ═══════════════════════════════════════════════════════════════

    def _get_current_season(self) -> str:
        if CURRENT_MONTH in [6, 7, 8, 9]:
            return "Kharif"
        elif CURRENT_MONTH in [10, 11, 12, 1]:
            return "Rabi"
        else:
            return "Summer (Pre-Kharif)"


if __name__ == "__main__":
    tool = WebIntelligenceTool()
    
    report = tool.generate_full_intelligence_report(
        lat=18.5204,
        lon=73.8567,
        crops=["Cotton", "Soybean", "Onion", "Coriander"],
        state="Maharashtra",
        district="Pune",
        total_acres=10.0,
    )
    
    output_path = os.path.join(
        os.path.dirname(os.path.dirname(__file__)),
        "module1_agentic", "agent_outputs", "web_intelligence_report.json"
    )
    # Save to agent_outputs in same dir
    save_path = os.path.join(os.path.dirname(__file__), "..", "agent_outputs", "web_intelligence_report.json")
    os.makedirs(os.path.dirname(save_path), exist_ok=True)
    with open(save_path, "w") as f:
        json.dump(report, f, indent=2, default=str)
    print(f"Report saved to {save_path}")