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from fastapi import FastAPI, File, UploadFile, HTTPException
from fastapi.responses import JSONResponse
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing import image
import numpy as np
import logging
from PIL import Image
import io
# Configure logging
logging.basicConfig(level=logging.DEBUG)
# Initialize FastAPI app
app = FastAPI()
# Load your trained model
model = load_model('model.h5')
class_names = ['Normal', 'bacteria', 'virus']
def preprocess_image(img, target_size):
"""Resize and preprocess the image for the model."""
if img.mode != "RGB":
img = img.convert("RGB")
img = img.resize(target_size)
img_array = image.img_to_array(img)
img_array = np.expand_dims(img_array, axis=0) # Add batch dimension
return img_array
@app.post("/predict")
async def predict(file: UploadFile = File(...)):
if not file:
raise HTTPException(status_code=400, detail="No file provided")
try:
# Read the file's content into a BytesIO object
img_bytes = io.BytesIO(await file.read())
# Use PIL to open the image
img = Image.open(img_bytes)
img_array = preprocess_image(img, (224, 224))
# Make prediction
predictions = model.predict(img_array)
predicted_class = np.argmax(predictions, axis=1)
# Return the prediction
predictions = {
'class': class_names[predicted_class[0]],
'confidence': float(predictions[0][predicted_class[0]])
}
return JSONResponse(content=predictions)
except Exception as e:
logging.debug(f"Error processing the file: {str(e)}")
raise HTTPException(status_code=500, detail=f"Error processing the file: {str(e)}")
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