prediction.py

import streamlit as st
import pandas as pd
import numpy as np
from PIL import Image
import os
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.models import load_model


# Load the Models

model = load_model('model.h5')
img_size = (64, 64)
# Define a function to preprocess the input image
def preprocess_input_image(img_path):
    img = image.load_img(img_path, target_size=img_size)
    img1 = image.load_img(img_path)
    x = image.img_to_array(img)
    x = np.expand_dims(x, axis=0)
    x /= 255.
    return x, img1

import os
import torch
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from datasets import load_dataset

# Define the path to the dataset
dataset_path = 'andrewsunanda/fast_food_image_classification'

# Load the dataset from Hugging Face
dataset = load_dataset(dataset_path)

# Define the batch size and image size
batch_size = 256
img_size = (64, 64)

# Define the paths to the train, validation, and test folders
train_path = os.path.join(dataset_path, 'Train')
valid_path = os.path.join(dataset_path, 'Valid')
test_path = os.path.join(dataset_path, 'Test')

# Define the transforms for the dataset
transform = transforms.Compose([
    transforms.Resize(img_size),
    transforms.ToTensor(),
])

# Load the training dataset
train_dataset = dataset['train']
train_dataset = train_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)

# Load the validation dataset
valid_dataset = dataset['validation']
valid_dataset = valid_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=False)

# Load the testing dataset
test_dataset = dataset['test']
test_dataset = test_dataset.map(lambda x: {'image': transform(x['image']), 'label': x['label']})
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# Create data generators for training, validation, and testing
train_datagen = ImageDataGenerator(
    rescale=1./255, 
    horizontal_flip=True
)

valid_datagen = ImageDataGenerator(
    rescale=1./255
)
test_datagen = ImageDataGenerator(
    rescale=1./255
)

train_generator = train_datagen.flow_from_directory(
    train_path, 
    target_size=img_size, 
    batch_size=batch_size, 
class_mode='categorical'
)

valid_generator = valid_datagen.flow_from_directory(
    valid_path, 
    target_size=img_size, 
    batch_size=batch_size, 
    class_mode='categorical'
)

test_generator = test_datagen.flow_from_directory(
    test_path,                                                 
    target_size=img_size, 
    batch_size=batch_size, 
    class_mode='categorical'
)

   
class_names = list(train_generator.class_indices.keys())
train_classes = pd.Series(train_generator.classes)
test_classes = pd.Series(test_generator.classes)
valid_classes = pd.Series(valid_generator.classes)

def run():

    st.title('Fast Food Image Prediction')


    with st.form(key='form_food'):
        uploaded_file = st.file_uploader("Choose an image file", type=["jpg", "jpeg", "png"])
        
        submitted = st.form_submit_button('Predict')


    
    if submitted:
        # Transform Inference-Set
        if uploaded_file is not None:
            # Preprocess the input image
            img = Image.open(uploaded_file)
            x = np.array(img.resize(img_size))/255.
            x = np.expand_dims(x, axis=0)
        
        # Make predictions on the input image
        preds = model.predict(x, verbose=0)
        pred_class = np.argmax(preds)
        pred_class_name = class_names[pred_class]
        
        # Display the input image and prediction result
        st.image(img, caption=f"Predicted Class: {pred_class_name}", use_column_width=True)



if __name__ == '__main__':
    run()