app.py

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)}"}
        )