Spaces:
Running
Running
File size: 3,107 Bytes
366d650 30c5114 ed89f9e 30c5114 ed89f9e 30c5114 ed89f9e 30c5114 ed89f9e 30c5114 ed89f9e 30c5114 ed89f9e 30c5114 216ee15 30c5114 ed89f9e 30c5114 216ee15 30c5114 216ee15 30c5114 cf9bcc7 30c5114 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 | 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) |