import os
from google import genai
from dotenv import load_dotenv
from PIL import Image

load_dotenv()

class ClaimsIntakeAgent:
    def __init__(self):
        api_key = os.getenv("GEMINI_API_KEY")
        if not api_key:
            print("Warning: GEMINI_API_KEY not found in environment variables.")
        else:
            self.client = genai.Client(api_key=api_key)

    def process_claim(self, user_input, image=None, document_path=None):
        """
        Processes a user claim report.
        Args:
            user_input (str): The user's description of the accident.
            image (PIL.Image): Optional image of the damage.
            document_path (str): Optional path to a uploaded document (PDF/Doc).
        Returns:
            str: The agent's response.
        """
        if not hasattr(self, 'client'):
             return "Error: Gemini API key not configured. Please check your .env file."

        from utils import convert_doc_to_markdown
        doc_content = ""
        if document_path:
            doc_content = convert_doc_to_markdown(document_path)
            doc_content = f"\n\n**Uploaded Document Content:**\n{doc_content}\n"
        print("Extracted content:",doc_content)
        prompt = f"""
        You are an AI assistant for an auto insurance company. Your goal is to help users report vehicle claims and provide a preliminary assessment for the back-office.
        Analyze the user's input, any provided images, and any uploaded document content.
        
        If an image is provided, estimate the damage severity and cost (in GBP) based on visual inspection.
        Extract vehicle information if possible.
        Analyze the text and documents for consistency and potential fraud indicators.
        
        **Uploaded Document Content:**
        {doc_content}
        
        Return a structured response in the following format:
        
        **Claim Assessment**
        *   **Submitter Name:** [Name if found in text/docs, else 'Anonymous']
        *   **Vehicle:** [Vehicle Make/Model/Year if identifiable, else 'Unknown']
        *   **Damage Severity:** [Minor/Moderate/Severe]
        *   **Estimated Repair Cost:** [Amount in £]
        *   **Fraud Risk:** [Low/Medium/High] - [Brief reason]
        *   **Adjuster Classification:** [Junior Adjuster/Senior Adjuster] (Route complex/high-risk/high-value >£5000 claims to Senior)
        *   **Policy Check:** Coverage appears valid (Simulated).
        *   **Summary:** [Concise summary of the incident and findings]
        *   **Next Steps:** [Brief instruction to user]
        
        If no image is provided, ask the user to upload one for a better assessment, but still acknowledge the report.
        """
        
        try:
            inputs = [prompt, user_input]
            if image:
                inputs.append(image)
            
            response = self.client.models.generate_content(
                model='gemini-2.5-flash',
                contents=inputs
            )
            return response.text
        except Exception as e:
            return f"Error processing claim: {str(e)}"

    def analyze_document(self, file_path):
        """
        Analyzes an uploaded document (PDF/Image) for claim info.
        For MVP, this handles images primarily, but could be expanded for PDFs.
        """
        # Placeholder for document analysis logic using Gemini
        return "Document received. Analysis feature is in beta (MVP)."