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51b7ac1 38d0aea 51b7ac1 38d0aea 51b7ac1 38d0aea 51b7ac1 38d0aea 51b7ac1 38d0aea 51b7ac1 | 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 | from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import numpy as np
import cv2
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import to_categorical
import os
# FastAPI app setup
app = FastAPI()
# Load the pre-trained model
model_path = 'sample.h5'
if not os.path.exists(model_path):
raise FileNotFoundError(f"Model file '{model_path}' not found. Ensure it is available in the working directory.")
model = load_model(model_path)
# Predefined image size for prediction
size = 100
# FastAPI Model for input data
class ImageData(BaseModel):
image_path: str # Path to image file
@app.get("/")
async def read_root():
"""
Root endpoint for checking if the API is running.
"""
return {"message": "FastAPI app for pothole detection is running successfully!"}
# Prediction Endpoint
@app.post("/predict/")
async def predict(data: ImageData):
"""
Predict whether the input image is a pothole or not.
"""
# Validate the image path
if not os.path.exists(data.image_path):
raise HTTPException(status_code=400, detail=f"Image file '{data.image_path}' not found.")
try:
# Load the image and preprocess
image = cv2.imread(data.image_path, 0)
if image is None:
raise ValueError("Failed to read the image. Please check the file format and path.")
image = cv2.resize(image, (size, size))
image = np.asarray(image).reshape(1, size, size, 1)
# Make prediction
prediction = model.predict(image)
predicted_class = np.argmax(prediction, axis=1)
# Return prediction result
return {"predicted_class": int(predicted_class[0])}
except Exception as e:
# Handle unexpected errors
raise HTTPException(status_code=500, detail=f"Error during prediction: {str(e)}")
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