import { SLMDataExtractor } from "../dataExtractors/SLMDataExtractor.js";
import { Utility } from "../utils/Utility.js";
import { DataInjector } from "../dataInjectors/DataInjector.js";
import { ExtractionReponse } from "../types/ExtractionResponse.js";
import { BasicLLMInferenceEngine, BasicInferenceConfig } from "@optimaxer/web-core";

export class FormEngine {
    private dataExtractor: SLMDataExtractor;
    private dataInjector: DataInjector;
    private llmInstance!: BasicLLMInferenceEngine;

    // Default configuration object
    private static defaultConfig: BasicInferenceConfig = {
        model: 'gemma',
        inferenceEngine: 'mediapipe',
        mode: 'local',
        devMode: true,
        connectorUrl: '',
        fallBackModel: 'openai-gpt-4o-mini',
        apiKey: ''
    };

    constructor(config: Partial<BasicInferenceConfig> = {}) {
        const finalConfig = Object.assign({}, FormEngine.defaultConfig, config);
        this.llmInstance = new BasicLLMInferenceEngine(finalConfig);
        this.dataExtractor = new SLMDataExtractor()
        this.dataInjector = new DataInjector()
    }
    /**
     * setup
     * @param config -   The configuration for the Form Engine setup.
     * @returns Response
     * 
     * This function initializes the Form Engine by creating an instance of the InferenceEngine.
     */
    static async setup(config: Partial<BasicInferenceConfig> = {}): Promise<FormEngine> {
        const instance = new FormEngine();
        const finalConfig = Object.assign({}, FormEngine.defaultConfig, config);

        // Initialize the InferenceEngine
        instance.llmInstance = await BasicLLMInferenceEngine.init(finalConfig);

        if(instance.llmInstance) {
            return instance;
        } else {
            return instance;
        }
    }

    /**
     * extract
     * @param formSchema 
     * @param text 
     * @returns { [key: string]: any }
     * 
     * This function extracts data from the given text using the SLM model and injects the extracted data into the form schema.
     */
    async extract(formSchema: { [key: string]: any }, text: string = ''): Promise<ExtractionReponse> {
        try {
            if(text.length < 1){
                // Access the clipboard data
                text = await navigator.clipboard.readText();
                console.log("Clipboard Text", text);
    
                // Ensure the clipboardText is not empty
                if (!text) {
                    console.error("Clipboard is empty or access is denied.");
                    return new ExtractionReponse(400,{}, 'Clipboard is empty or access is denied.');
                }
            }
            let injectedData: { [key: string]: any } = {};
            if(Utility.hasNestedObject(formSchema)){
                const extracted_JSON: { [key: string]: any } = await this.dataExtractor.extractDataForComplexObject(text,formSchema, this.llmInstance);
                return new ExtractionReponse(200, extracted_JSON, 'Complex Extractions');
            
            }else{ 
                const extracted_JSON: { [key: string]: any } = await this.dataExtractor.extractData(text,formSchema, this.llmInstance);
                const flatten_JSON = Utility.flattenJsonObject(extracted_JSON);
                injectedData = this.dataInjector.injectData(flatten_JSON, formSchema);
            }
            return new ExtractionReponse(200, injectedData, 'Data Extraction Successful');
        } catch (error) {
            console.error("Error in data extraction", error);
            return new ExtractionReponse(400,{}, 'Error in data extraction');
        }
        
    }

    /**
     * rawExtract
     * This function extracts data from the given text using the SLM model. And does not require a schema.
     * @param text The text to extract data from. 
     * @returns { [key: string]: any }
     */
    async rawExtract(text: string = ''): Promise<ExtractionReponse> {
        try {
            if(text.length < 1){
                // Access the clipboard data
                text = await navigator.clipboard.readText();
                console.log("Clipboard Text", text);
    
                // Ensure the clipboardText is not empty
                if (!text) {
                    console.error("Clipboard is empty or access is denied.");
                    return new ExtractionReponse(400,{}, 'Clipboard is empty or access is denied.');
                }
            }
            const extracted_JSON: { [key: string]: any } = await this.dataExtractor.extractData(text, {}, this.llmInstance);
            const flatten_JSON = Utility.flattenJsonObject(extracted_JSON);
            return new ExtractionReponse(200, flatten_JSON, 'Data Extraction Successful');
        } catch (error) {
            console.error("Error in raw data extraction", error);
            return new ExtractionReponse(400,{}, 'Error in raw data extraction');
        }
        
    }

}