Spaces:
Sleeping
Sleeping
File size: 2,235 Bytes
0ea3bba 2d6f3da 0ea3bba 3f1fbb7 0ea3bba | 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 | from fastapi import FastAPI, File, UploadFile
from fastapi.responses import JSONResponse
import tensorflow as tf
import numpy as np
import shutil
import os
# Initialize FastAPI app
app = FastAPI()
# Class labels
class_labels = {
0: 'Baked Potato', 1: 'Burger', 2: 'Crispy Chicken', 3: 'Donut', 4: 'Fries',
5: 'Hot Dog', 6: 'Jalapeno', 7: 'Kiwi', 8: 'Lemon', 9: 'Lettuce',
10: 'Mango', 11: 'Onion', 12: 'Orange', 13: 'Pizza', 14: 'Taquito',
15: 'Apple', 16: 'Banana', 17: 'Beetroot', 18: 'Bell Pepper', 19: 'Bread',
20: 'Cabbage', 21: 'Carrot', 22: 'Cauliflower', 23: 'Cheese',
24: 'Chilli Pepper', 25: 'Corn', 26: 'Crab', 27: 'Cucumber',
28: 'Eggplant', 29: 'Eggs', 30: 'Garlic', 31: 'Ginger', 32: 'Grapes',
33: 'Milk', 34: 'Salmon', 35: 'Yogurt'
}
# Load the trained model
model = tf.keras.models.load_model("model_unfreezeNewCorrectpredict.keras")
# Image preprocessing function
def load_and_prep_image(file_path, img_shape=224):
img = tf.io.read_file(file_path)
img = tf.image.decode_image(img, channels=3)
img = tf.image.resize(img, size=[img_shape, img_shape])
img = tf.expand_dims(img, axis=0)
return img
# Predict label function
def predict_label(model, image_path, class_names):
img = load_and_prep_image(image_path, img_shape=224)
pred = model.predict(img)
pred_class_index = np.argmax(pred, axis=1)[0]
pred_class_name = class_names[pred_class_index]
return pred_class_name
@app.get("/")
def read_root():
return {"message": "This is My Nutrionguid App"}
# API endpoint for prediction
@app.post("/predict")
async def predict_image(file: UploadFile = File(...)):
try:
# Save the uploaded file
file_location = f"./temp_{file.filename}"
with open(file_location, "wb") as f:
shutil.copyfileobj(file.file, f)
# Predict the label
prediction = predict_label(model, file_location, class_labels)
# Remove the temporary file
os.remove(file_location)
return {"predicted_label": prediction}
except Exception as e:
return JSONResponse(
status_code=500,
content={"error": f"An error occurred: {str(e)}"}
)
|