#!/usr/bin/env python3
"""
Address Detection and Summarization API for Hugging Face Spaces
"""

from fastapi import FastAPI, HTTPException, Header
from fastapi.middleware.cors import CORSMiddleware
from pydantic import BaseModel, Field
import os
from enum import Enum
from typing import Dict, Optional, Annotated
import logging
from dotenv import load_dotenv
import re

# Load environment variables
load_dotenv()

# Configure logging
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)

app = FastAPI(title="Address Detection and Summarization API")

# Add CORS middleware for Hugging Face Spaces
app.add_middleware(
    CORSMiddleware,
    allow_origins=["*"],
    allow_credentials=True,
    allow_methods=["*"],
    allow_headers=["*"],
)

class Actions(Enum):
    DETECT_ADDRESS = "detect_address"
    SUMMARIZE = "summarize"

# Request model
class RequestModel(BaseModel):
    entity_urn: str = Field(..., description="Unique identifier for the entity")
    content: str = Field(..., description="Content to be processed")

# Response models
class ResponseModel(BaseModel):
    message: str = Field(description="Result of the operation, e.g., 'success' or 'failure'")
    result: str = Field(description="summary in case of summarization, or || separated addresses in case of address detection")
    action_type: Actions = Field(..., description="Type of action performed")
    entity_urn: str = Field(..., description="Unique identifier for the entity")
    sentiment: Optional[Dict] = Field(default=None, description="Sentiment analysis result")


@app.get("/")
async def root():
    return {
        "message": "Address Detection and Summarization API",
        "version": "1.0.0",
        "status": "healthy",
        "endpoints": {
            "address_detection": "/address-detection",
            "summarization": "/summarize",
            "health": "/health",
            "docs": "/docs"
        }
    }

@app.get("/health")
async def health_check():
    return {
        "status": "healthy",
        "service": "Address Detection and Summarization API",
        "version": "1.0.0"
    }

def extract_address(text: str) -> str:
    address_pattern = r'\d+\s+[A-Za-z\s]+(?:Street|St|Avenue|Ave|Road|Rd|Boulevard|Blvd|Lane|Ln|Drive|Dr|Court|Ct|Place|Pl)'
    match = re.search(address_pattern, text, re.IGNORECASE)
    return match.group().strip() if match else ""

def extract_summary(text: str) -> str:
    sentences = text.split(".")
    return ". ".join(sentences[:3]).strip() + "." if sentences else ""


@app.post("/address-detection", response_model=ResponseModel)
async def address_detection(
    request: RequestModel,
    openai_api_key: Annotated[str, Header(alias="x-api-key")]
):
    """Endpoint for address detection"""
    try:
        logger.info(f"Processing address detection for entity_urn: {request.entity_urn}")
        
        if not request.content.strip():
            return ResponseModel(
                message="failure",
                result="Content is empty",
                action_type=Actions.DETECT_ADDRESS,
                entity_urn=request.entity_urn,
                sentiment=None
            )
        
        addresses = extract_address(request.content)
        
        return ResponseModel(   
            message="success",
            result=addresses,
            action_type=Actions.DETECT_ADDRESS,
            entity_urn=request.entity_urn,
            sentiment=None
        )
    
    except Exception as e:
        logger.error(f"Address detection endpoint error: {str(e)}")
        return ResponseModel(
            message="failure",
            result=f"Error: {str(e)}",
            action_type=Actions.DETECT_ADDRESS,
            entity_urn=request.entity_urn,
            sentiment=None
        )

@app.post("/summarize", response_model=ResponseModel)
async def summarize(
    request: RequestModel,
    openai_api_key: Annotated[str, Header(alias="x-api-key")]
):
    """Endpoint for text summarization and sentiment analysis"""
    try:
        logger.info(f"Processing summarization for entity_urn: {request.entity_urn}")
        
        if not request.content.strip():
            return ResponseModel(   
                message="failure",
                result="Content is empty",
                action_type=Actions.SUMMARIZE,
                entity_urn=request.entity_urn,
                sentiment={}
            )
        
        summary = extract_summary(request.content)

        sentiment = {
            "label": "POSITIVE",
            "score": 0.95
        }

        
        return ResponseModel(
            message="success",
            result=summary,
            sentiment=sentiment,
            action_type=Actions.SUMMARIZE,
            entity_urn=request.entity_urn   
        )
    
    except Exception as e:
        logger.error(f"Summarization endpoint error: {str(e)}")
        return ResponseModel(
            message="failure",
            result=f"Error: {str(e)}",
            sentiment={},
            action_type=Actions.SUMMARIZE,
            entity_urn=request.entity_urn
        )

if __name__ == "__main__":
    import uvicorn
    # Use port 7860 for Hugging Face Spaces, fallback to 8500 for local
    port = int(os.getenv("PORT", 7860))
    uvicorn.run(app, host="0.0.0.0", port=port)