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import streamlit as st
from transformers import pipeline
from PIL import Image
from io import BytesIO
from transformers import GPT2LMHeadModel, GPT2Tokenizer
import random
st.set_page_config(layout="wide", page_title="Image Classification App")
st.write("## Image Food Classification App")
st.sidebar.write("## Upload and download :gear:")
# Initialize image classification and recipe generation models
image_classifier = pipeline("image-classification", model="mjsp/sweet")
recipe_model = GPT2LMHeadModel.from_pretrained("gpt2")
recipe_tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
MAX_FILE_SIZE = 5 * 1024 * 1024 # 5MB
def convert_image(img):
buf = BytesIO()
img.save(buf, format="PNG")
byte_im = buf.getvalue()
return byte_im
def fix_image(upload):
image = Image.open(upload)
st.image(image, caption="Original Image", use_column_width=True)
# You'll need to add the 'rembg' functionality or replace it with your own image processing logic
# fixed = your_image_processing_function(image)
# st.image(fixed, caption="Fixed Image", use_column_width=True)
st.sidebar.markdown("\n")
st.sidebar.download_button("Download fixed image", convert_image(fixed), "fixed.png", "image/png")
def generate_recipe(title, max_length=200):
# Replace this with your actual dataset
dataset = {
"Gulab Jamun": {
"ingredients": ["milk powder", "ghee", "rose water", "saffron", "cardamom", "sugar syrup"],
"recipe": "Instructions for making Gulab Jamun...",
},
"Jalebi": {
"ingredients": ["all-purpose flour", "yogurt", "sugar", "water", "saffron strands", "cardamom powder", "ghee or oil for frying"],
"recipe": "Instructions for making Jalebi...",
},
"Rasgulla": {
"ingredients": ["milk", "sugar", "lemon juice", "rose water"],
"recipe": "Instructions for making Rasgulla...",
}
}
if title in dataset:
selected_entry = dataset[title]
title = title
ingredients = selected_entry["ingredients"]
else:
title = "Default Recipe Title"
ingredients = []
input_text = f"Title: {title}\nIngredients: {', '.join(ingredients)}\n Instructions:"
input_ids = recipe_tokenizer.encode(input_text, return_tensors="pt")
output = recipe_model.generate(input_ids, max_length=max_length, num_return_sequences=1)
generated_recipe = recipe_tokenizer.decode(output[0], skip_special_tokens=True)
return generated_recipe
col1, col2 = st.columns(2)
my_upload = st.sidebar.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
if my_upload:
st.image(my_upload, caption="Uploaded Image", use_column_width=True)
if st.sidebar.button("Classify"):
st.sidebar.text("Classifying...")
image = Image.open(my_upload)
try:
classification_result = image_classifier(image)
top_prediction = classification_result[0]
label = top_prediction['label']
score = top_prediction['score']
st.sidebar.text("Top Prediction:")
st.sidebar.text(f"Label: {label}, Score: {score:.3f}")
except Exception as e:
st.error(f"Error during classification: {str(e)}")
if my_upload.size > MAX_FILE_SIZE:
st.error("The uploaded file is too large. Please upload an image smaller than 5MB.")
else:
fix_image(my_upload)
# Recipe generation based on selected item
st.write("## Recipe Generation")
selected_item = st.selectbox("Select a food item", ["Gulab Jamun", "Jalebi", "Rasgulla"])
if st.button("Generate Recipe"):
generated_recipe = generate_recipe(selected_item, max_length=200)
st.write(f"Recipe for {selected_item}:\n{generated_recipe}")
# Add some descriptions and instructions
st.sidebar.markdown("### Instructions")
st.sidebar.markdown("1. Upload an image.")
st.sidebar.markdown("2. Click the 'Classify' button to get the classification results.")
st.sidebar.markdown("3. Select a food item to generate a recipe.")
st.sidebar.markdown("4. Click the 'Generate Recipe' button to get the recipe.")
# Display a placeholder for the main content
st.write("Please upload an image and use the sidebar to classify it and generate a recipe.")
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