import uvicorn, traceback ,json, time, httpx
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel

################################################ Import Main Classes ################################################
from tools.tools import UniverseToolsClass
from modules.ubl.da.da_main import UBLClass
from modules.bat.bat_main import BATClass
from modules.bangla_ocr.bangla_ocr_main import BanglaOcrClass
from modules.face_detection.face_detection_main import FaceDetectionClass
from modules.face_recognition.face_recognition_main import facialRecognitionClass
from modules.bevarage_indrasty.bevarage_indrasty_main import BevarageIndrastyClass
from modules.material_detection.material_main import MaterialDetectionClass
from modules.laver_bazar_ocr.laver_bazar_ocr_main import LaverBazarOcrClass 
from modules.hotspot.hotspot_main import HotspotCalculateClass
from modules.ekyc.ekyc_main import EKYCClass
from modules.nlp.nlp_main import NlpClass
from modules.mt_house_ocr.mt_house_ocr_main import MTHouseOCRClass
from modules.nlp_zerocal.nlp_zerocal_main import NlpZeroCalClass
from modules.nlp_maya.nlp_maya_main import NlpMayaClass
from modules.ubl.category import categoryClass
from modules.speechanalyzer.speechanalyzer_main import SpeechAnalyzerClass # Logicai
from modules.nlp_conversational_guidance.nlp_conversational_guidance_main import NlpConversationalGuidanceClass
from modules.speakmetric.speakmetric_main import SpeakMetricClass
from modules.marge_speak_conver.marge_speak_conver_main import MargeSpeakConverClass
from modules.international_image_analysis.category import IntImgAnaCategoryClass
from modules.international_image_analysis_ubl.category_iia_ubl import IntImgAnaCategoryClass_UBL

################################################ Import Model Classes ################################################
from tools.models_handler import ModelsUBL, ModelsBAT, ModelFace, ModelBATHotspot, ModelBevarageIndrasty, ModelMaterial, ModelMemoOcr, ModelSpeechAnalyzer, ModelIntImgAna, ModelMthouseOcr

################################################ Create Objects for ALL the Models ################################################
face_model = ModelFace()
ubl_models = ModelsUBL()
bat_models = ModelsBAT()
memo_ocr_model = ModelMemoOcr()
demo_gp_model = ModelMaterial()
bevarage_indrasty_model = ModelBevarageIndrasty()
bat_hotspot_model = ModelBATHotspot()
speechanalyzer_model = ModelSpeechAnalyzer()
int_img_ana_model = ModelIntImgAna()
mt_house_ocr_model = ModelMthouseOcr()

################################################ Create Objects for ALL the Classes ################################################
universe_tools_object = UniverseToolsClass()
ubl_class_object = UBLClass()
bat_class_object = BATClass()
bevarage_indrasty_class_object = BevarageIndrastyClass()
facial_recognition_class_object = facialRecognitionClass()
face_detection_class_object = FaceDetectionClass()
hotspot_class_object = HotspotCalculateClass()
material_detection_class_object = MaterialDetectionClass()
bangla_ocr_class_object = BanglaOcrClass()
laver_bazar_ocr_class_object = LaverBazarOcrClass()
ekyc_class_object = EKYCClass()
nlp_class_object = NlpClass()
mt_house_ocr_class_object = MTHouseOCRClass()
nlp_zerocal_class_object = NlpZeroCalClass()
nlp_maya_class_object = NlpMayaClass()
category_class_object = categoryClass()
speechanalyzer_class_object = SpeechAnalyzerClass()
nlp_conversational_guidance_class_object = NlpConversationalGuidanceClass()
speakmetric_class_object = SpeakMetricClass()
marge_speak_conver_class_object = MargeSpeakConverClass()
int_img_ana_category_class_object = IntImgAnaCategoryClass()
int_img_ana_category_class_ubl_object = IntImgAnaCategoryClass_UBL()

# Create a logger
logger = universe_tools_object.setup_logger()

# Load data
appId_dic = universe_tools_object.load_data('tools')['appId']

app = FastAPI()

class ObjectDetection(BaseModel):
    appId : str
    appName : str
    url : list
    company : str = None
    area : str = None
    age : str = None
    
class UnileverModel(BaseModel):
    outlet: dict
    job: list

################################################ BAT ################################################
@app.get("/status/bat", tags=["BAT Status"])
async def status():
    url = "https://batnlp.ngrok.app/bat/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "BAT Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "BAT Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "BAT returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "BAT AI Server unreachable",
            "error": str(e)
        }

@app.post("/bat/", tags=["BAT"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId==appId_dic['bat']:
            result = await bat_class_object.bat_main_func(images=items.url,models=bat_models)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY

    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Facial Recognition ################################################
@app.get("/status/faceRecognition", tags=["Facial Recognition Status"])
async def status():
    url = "https://batnlp.ngrok.app/faceRecognition/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Facial Recognition Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Facial Recognition Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Facial Recognition returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Facial Recognition AI Server unreachable",
            "error": str(e)
        }

@app.post("/faceRecognition/", tags=["Facial Recognition"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId==appId_dic['facialRecognition']:
            result = await facial_recognition_class_object.face_recognition_main_func(items.url)
            universe_tools_object.monitor(result)
            BODY["ai_feedback"] = result
            BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Face Detection ################################################
@app.get("/status/faceDetection", tags=["Face Detection Status"])
async def status():
    url = "https://batnlp.ngrok.app/faceDetection/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Face Detection Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Face Detection Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Face Detection returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Face Detection AI Server unreachable",
            "error": str(e)
        }

@app.post("/faceDetection/", tags=["Face Detection"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId==appId_dic['faceDetection']:
            result = await face_detection_class_object.face_det_main(items.url[0]['original'], face_model.faceModel)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Hotspot ################################################
@app.get("/status/hotspot", tags=["Hotspot Status"])
async def status():
    url = "https://batnlp.ngrok.app/hotspot/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Hotspot Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Hotspot Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Hotspot returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Hotspot AI Server unreachable",
            "error": str(e)
        }


@app.post("/hotspot/", tags=["Hotspot"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId==appId_dic['Hotspot']:
            result = await hotspot_class_object.hotspot_main_func(items.url[0]['original'], bat_hotspot_model.BATHotspotModel)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Bangla OCR ################################################
@app.get("/status/banglaOcr", tags=["Bangla OCR Status"])
async def status():
    url = "https://batnlp.ngrok.app/banglaOcr/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Bangla OCR Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Bangla OCR Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Bangla OCR returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Bangla OCR AI Server unreachable",
            "error": str(e)
        }

@app.post("/banglaOcr/", tags=["Bangla OCR"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId==appId_dic['banglaOcr']:
            result = await bangla_ocr_class_object.bng_ocr_main_func(items.url[0]['original'])
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Nid Ocr ################################################
@app.get("/status/nidOcr", tags=["Nid Ocr Status"])
async def status():
    url = "https://batnlp.ngrok.app/nidOcr/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Nid Ocr Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Nid Ocr Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Nid Ocr returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Nid Ocr AI Server unreachable",
            "error": str(e)
        }

@app.post("/nidOcr/", tags=["Nid Ocr"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['ekyc']:
            result = await ekyc_class_object.ekyc_main_func(items.url[0]['original'],items.url[1]['original'])
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Memo OCR ################################################
@app.get("/status/ocr", tags=["Memo OCR Status"])
async def status():
    url = "https://batnlp.ngrok.app/ocr/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Memo OCR Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Memo OCR Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Memo OCR returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Memo OCR AI Server unreachable",
            "error": str(e)
        }

@app.post("/ocr/", tags=["Memo OCR"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['memoOcr']:
            result = await laver_bazar_ocr_class_object.laver_bazar_ocr_main(items.url[0]['original'], memo_ocr_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ NLP ################################################
@app.get("/status/nlpBat", tags=["NLP Status"])
async def status():
    url = "https://batnlp.ngrok.app/nlpBat/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "NLP Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "NLP Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "NLP returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "NLP AI Server unreachable",
            "error": str(e)
        }

@app.post("/nlpBat/", tags=["NLP"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['nlp']:
            result = await nlp_class_object.nlp_main_func(items.url[0]['original'], models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Bevarage Indrasty Analysis ################################################
@app.get("/status/fridgeDetection", tags=["Bevarage Indrasty Analysis Status"])
async def status():
    url = "https://batnlp.ngrok.app/fridgeDetection/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Bevarage Indrasty Analysis Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Bevarage Indrasty Analysis Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Bevarage Indrasty Analysis returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Bevarage Indrasty Analysis AI Server unreachable",
            "error": str(e)
        }

@app.post("/fridgeDetection/", tags=["Bevarage Indrasty Analysis"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['bevarage_indrasty']:
            result = await bevarage_indrasty_class_object.bevarage_indrasty_main_func(items.url, bevarage_indrasty_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
            BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Material Detection ################################################
@app.get("/status/materialDetection", tags=["Material Detection Status"])
async def status():
    url = "https://batnlp.ngrok.app/materialDetection/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Material Detection Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Material Detection Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Material Detection returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Material Detection AI Server unreachable",
            "error": str(e)
        }

@app.post("/materialDetection/", tags=["Material Detection"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['materialDetection']:
            result = await material_detection_class_object.material_main_func(items.url[0]['original'], demo_gp_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ MT House OCR ################################################
@app.get("/status/mTHouseOCR", tags=["MT House OCR Status"])
async def status():
    url = "https://batnlp.ngrok.app/mTHouseOCR/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "MT House OCR Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "MT House OCR Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "MT House OCR returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "MT House OCR AI Server unreachable",
            "error": str(e)
        }

@app.post("/mTHouseOCR/", tags=["MT House OCR"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['tableToJson']:
            result = await mt_house_ocr_class_object.mt_house_ocr_main_func(items.url, model=mt_house_ocr_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY, ensure_ascii=False)}\n")
        return BODY    
        # return response
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ NLP Zero Cal ################################################
@app.get("/status/nlpZeroCal", tags=["NLP Zero Cal Status"])
async def status():
    url = "https://batnlp.ngrok.app/nlpZeroCal/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "NLP Zero Cal Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "NLP Zero Cal Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "NLP Zero Cal returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "NLP Zero Cal AI Server unreachable",
            "error": str(e)
        }

@app.post("/nlpZeroCal/", tags=["NLP Zero Cal"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['nlpZeroCal']:
            result = await nlp_zerocal_class_object.nlp_zerocal_main_func(items.url[0]['original'], models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ NLP Maya ################################################
@app.get("/status/nlpMaya", tags=["NLP Maya Status"])
async def status():
    url = "https://batnlp.ngrok.app/nlpMaya/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "NLP Maya Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "NLP Maya Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "NLP Maya returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "NLP Maya AI Server unreachable",
            "error": str(e)
        }

@app.post("/nlpMaya/", tags=["NLP Maya"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['nlpmaya']:
            result = await nlp_maya_class_object.nlp_maya_main_func(items.url[0]['original'], items.area, items.age, models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ FMCG ################################################
@app.get("/status/fmcg", tags=["FMCG Status"])
async def status():
    url = "https://batnlp.ngrok.app/fmcg/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "FMCG Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "FMCG Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "FMCG returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "FMCG AI Server unreachable",
            "error": str(e)
        }

@app.post("/fmcg/", tags=["FMCG"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['fmcg']:
            result = await category_class_object.process_category(input_data=items.url, models=ubl_models)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ SPEECHFLOW ################################################
@app.get("/status/speechflow", tags=["SPEECHFLOW Status"])
async def status():
    url = "https://batnlp.ngrok.app/speechflow/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "SPEECHFLOW Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "SPEECHFLOW Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "SPEECHFLOW returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "SPEECHFLOW AI Server unreachable",
            "error": str(e)
        }

@app.post("/speechflow/", tags=["SPEECHFLOW"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['speechflow']:
            result = await speechanalyzer_class_object.speechanalyzer_main_func(items.url[0]['original'], models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Conversational Guidance ################################################
@app.get("/status/conversationalguidance", tags=["Conversational Guidance Status"])
async def status():
    url = "https://batnlp.ngrok.app/conversationalguidance/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Conversational Guidance Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Conversational Guidance Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Conversational Guidance returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Conversational Guidance AI Server unreachable",
            "error": str(e)
        }

@app.post("/conversationalguidance/", tags=["Conversational Guidance"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['nlpconversationalguidance']:
            result = await nlp_conversational_guidance_class_object.nlp_conversational_guidance_main_func(items.url[0]['original'], items.age, models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY
    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ SpeakMetric ################################################
@app.get("/status/speakmetric", tags=["SpeakMetric Status"])
async def status():
    url = "https://batnlp.ngrok.app/speakmetric/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "SpeakMetric Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "SpeakMetric Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "SpeakMetric returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "SpeakMetric AI Server unreachable",
            "error": str(e)
        }

@app.post("/speakmetric/", tags=["SpeakMetric"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['speakmetric']:
            result = await speakmetric_class_object.speakmetric_main_func(items.url[0]['original'], models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Domex ################################################
@app.get("/status/domex", tags=["Domex Status"])
async def status():
    url = "https://batnlp.ngrok.app/domex/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Domex Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Domex Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Domex returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Domex AI Server unreachable",
            "error": str(e)
        }

@app.post("/domex/", tags=["Domex"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['domex']:
            result = await marge_speak_conver_class_object.marge_speak_conver_main_func(items.url[0]['original'], models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ International Image Analysis ################################################
@app.get("/status/intimgana", tags=["International Image Analysis Status"])
async def status():
    url = "https://batnlp.ngrok.app/intimgana/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "International Image Analysis Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "International Image Analysis Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "International Image Analysis returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "International Image Analysis AI Server unreachable",
            "error": str(e)
        }

@app.post("/intimgana/", tags=["International Image Analysis"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['intimgana']:
            result = await int_img_ana_category_class_object.int_img_ana_process_category(input_data=items.url, models=int_img_ana_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ Pitch Monitoring ################################################
@app.get("/status/pitchMonitoring", tags=["Pitch Monitoring Status"])
async def status():
    url = "https://batnlp.ngrok.app/pitchMonitoring/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "Pitch Monitoring Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "Pitch Monitoring Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "Pitch Monitoring returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "Pitch Monitoring AI Server unreachable",
            "error": str(e)
        }

@app.post("/pitchMonitoring/", tags=["Pitch Monitoring"])
async def create_items(items: ObjectDetection):
    try:
        BODY = universe_tools_object.Body(items)
        logger.info(f"Request - {json.dumps(BODY)}")
        if items.appId== appId_dic['pitchMonitoring']:
            result = await speakmetric_class_object.speakmetric_main_func(items.url[0]['original'], models=speechanalyzer_model)
            universe_tools_object.monitor(result)
            if result:
                BODY["ai_feedback"] = result
                BODY['statusAI'] = True
        logger.info(f"Response - {json.dumps(BODY)}\n")
        return BODY    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

################################################ International Image Analysis UBL ################################################
@app.get("/status/intimgana_ubl", tags=["International Image Analysis UBL Status"])
async def status():
    url = "https://batnlp.ngrok.app/intimgana_ubl/"

    start = time.perf_counter()

    try:
        async with httpx.AsyncClient(timeout=5) as client:
            response = await client.post(url)

        latency = round(time.perf_counter() - start, 3)

        if response.status_code == 422:
            return {
                "status": "Server Running...",
                "message": "International Image Analysis UBL Module is Active...",
                "status_code": 200,
                "latency_sec": latency
            }

        elif response.status_code < 500:
            return {
                "status": "Server Running...",
                "message": "International Image Analysis UBL Module is Active...",
                "status_code": response.status_code,
                "latency_sec": latency
            }
        else:
            return {
                "status": "Server Error !!!",
                "message": "International Image Analysis UBL returned server error",
                "status_code": response.status_code,
                "latency_sec": latency
            }

    except httpx.RequestError as e:
        return {
            "status": "Server Down ..!!!",
            "message": "International Image Analysis UBL AI Server unreachable",
            "error": str(e)
        }

@app.post("/intimgana_ubl/", tags=["International Image Analysis UBL"])
async def create_items(items: UnileverModel):
    try:
        logger.info(f"Request - {json.dumps(items.model_dump())}")
        if True:
            result = await int_img_ana_category_class_ubl_object.int_img_ana_process_category_ubl(input_data=items, models=int_img_ana_model)
            universe_tools_object.monitor(result)
        logger.info(f"Response - {json.dumps(result)}\n")
        return result    
    except Exception as e:
        traceback.print_exc()
        raise HTTPException(status_code=500, detail="Internal Server Error")
    finally:
        universe_tools_object.refresh()

if __name__ == "__main__":
    uvicorn.run(app, host="127.0.0.1", port=5656)