main.py

from fastapi import FastAPI, HTTPException
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse
from utils.palmoil_classification import AfroPalmModel
from utils.image_preclassification import pre_classification
from utils.audio_generation import AudioGeneration
import logging
import uvicorn
import base64
import io
import numpy as np
from io import BytesIO
from pydantic import BaseModel
import os


# Logging
logging.basicConfig(level=logging.DEBUG,format='%(levelname)s: %(asctime)s - %(message)s', datefmt='%d-%b-%y %H:%M:%S')


description = """
## Welcome to the Red Palm Oil Adulteration Detection Backend Api
"""

class ImageRequest(BaseModel):
    imageURL:str

# Initialize FastAPI
app = FastAPI(title="Afro Red Palm Oil Project", description=description)

app.add_middleware(
    CORSMiddleware,
    allow_origins=['*'],
    allow_methods=['*'],
    allow_headers=["*"],
)

"""
    API Routes
"""
# Home route


@app.get('/')
async def home():
    return {description}

# red palm oil classification endpoint
@app.post("/predict")
async def predict(image_request: ImageRequest):
    # logging.info("Loading image")

    # # Decode base64 image string
    # decoded_image = base64.b64decode(image_request.image)

    # # Create a BytesIO object to read the image data
    # image_bytes = BytesIO(decoded_image)

    try:
        # Pre-classify image
        is_palm_oil, image_path = pre_classification(image_request.imageURL)

        logging.info("Pre-classification successful")
        
        if is_palm_oil:
            model = AfroPalmModel()
            prediction,confidence = model.predict(image_path)
            logging.debug(f"Prediction: {prediction}, Confidence: {confidence*100:.2f}%")

            # Generate audio
            # audio_generation = AudioGeneration(prediction=prediction, confidence=confidence*100, language=image_request.lang)
            # translated_text = audio_generation.ghanaian_language_translator()
            # translated_text_audiofile = audio_generation.text_to_audio(translated_text)

        else:
            logging.info("Image is not a red palm oil")
            return JSONResponse(status_code=418, content={"status": "error",
                                                     'error':"Image is not a red palm oil"})
            
        # model = AfroPalmModel()
        # prediction,confidence = model.predict(image_bytes)
        if os.path.isfile(image_path):
            # Remove the file after processing
            os.remove(image_path) 

        return {
            "status": "success",
            "result": prediction,
            "confidence": f"{confidence*100:.2f}",

        }
            # "audio":  FileResponse(path='final_result.wav', media_type="audio/mpeg", filename="final_result.wav")

    
    except Exception as e:
        logging.error(e)
        raise HTTPException(status_code=500, detail={"status": "error",
                                                     'error': str(e)})


if __name__ == '__main__':
    uvicorn.run(app, host="0.0.0.0", port="8000", debug=True)