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| import streamlit as st | |
| import tensorflow as tf | |
| import numpy as np | |
| from PIL import Image | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Load the saved model | |
| model_path = "fahrnphi_exam_project.keras" | |
| model = tf.keras.models.load_model(model_path) | |
| # Define the core prediction function | |
| def predict_ingredient(image): | |
| # Preprocess image | |
| image = image.resize((150, 150)) # Resize the image to 150x150 | |
| image = image.convert('RGB') # Ensure image has 3 channels | |
| image = np.array(image) | |
| image = np.expand_dims(image, axis=0) # Add batch dimension | |
| # Predict | |
| prediction = model.predict(image) | |
| # Apply softmax to get probabilities for each class | |
| probabilities = tf.nn.softmax(prediction, axis=1) | |
| # Map probabilities to ingredient classes | |
| class_names = ['Peperoni', 'Carrot', 'Garlic', 'Ginger', 'Jalapeno', 'Onion', 'Potato', 'Sweetpotato', 'Tomato'] | |
| probabilities_dict = {ingredient_class: round(float(probability), 2) for ingredient_class, probability in zip(class_names, probabilities.numpy()[0])} | |
| return probabilities_dict | |
| # Streamlit interface | |
| st.title("Ingredient Classifier") | |
| st.write("A simple MLP classification model for image classification using a pretrained model.") | |
| # Initialize session state for storing ingredients | |
| if 'ingredients' not in st.session_state: | |
| st.session_state['ingredients'] = [] | |
| # Upload image | |
| uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png"]) | |
| if uploaded_image is not None: | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption='Uploaded Image.', use_column_width=True) | |
| st.write("") | |
| st.write("Classifying...") | |
| predictions = predict_ingredient(image) | |
| # Display predictions as a DataFrame | |
| st.write("### Prediction Probabilities") | |
| df = pd.DataFrame(predictions.items(), columns=["Ingredient", "Probability"]) | |
| st.dataframe(df) | |
| # Display predictions as a pie chart | |
| st.write("### Prediction Chart") | |
| fig, ax = plt.subplots() | |
| ax.pie(df["Probability"], labels=df["Ingredient"], autopct='%1.1f%%', colors=plt.cm.Paired.colors) | |
| ax.set_title('Prediction Probabilities') | |
| st.pyplot(fig) | |
| # Automatically select the best guess (highest probability) | |
| best_guess = df.loc[df['Probability'].idxmax()]["Ingredient"] | |
| if st.button("Add Ingredient"): | |
| st.session_state.ingredients.append(best_guess) | |
| st.write(f"Added {best_guess} to ingredients list") | |
| # Display the ingredients added so far | |
| st.write("### Selected Ingredients") | |
| st.write(st.session_state.ingredients) | |
| # Finish button to finalize ingredient selection and find recipes | |
| if st.button("Finish"): | |
| st.write("Finding recipes...") | |
| # Placeholder: Replace with actual recipe finding logic | |
| def find_recipes(ingredients): | |
| # This is a mock function, replace with actual recipe finding logic | |
| sample_recipes = [ | |
| {"name": "Vegetable Stir Fry", "ingredients": ["Peperoni", "Carrot", "Onion"], "instructions": "Stir fry the vegetables in a hot pan with some oil."}, | |
| {"name": "Tomato Garlic Pasta", "ingredients": ["Tomato", "Garlic"], "instructions": "Cook pasta and mix with sautéed tomato and garlic."}, | |
| {"name": "Ginger Potato Soup", "ingredients": ["Ginger", "Potato"], "instructions": "Boil potatoes and ginger, then blend into a soup."}, | |
| {"name": "Jalapeno Onion Salad", "ingredients": ["Jalapeno", "Onion"], "instructions": "Mix chopped jalapeno and onion with some lime juice."}, | |
| {"name": "Sweetpotato Carrot Soup", "ingredients": ["Sweetpotato", "Carrot"], "instructions": "Boil sweetpotato and carrot, then blend into a soup."}, | |
| {"name": "Garlic Mashed Potatoes", "ingredients": ["Garlic", "Potato"], "instructions": "Boil potatoes, mash them with roasted garlic and butter."}, | |
| {"name": "Ginger Carrot Salad", "ingredients": ["Ginger", "Carrot"], "instructions": "Grate carrots and mix with finely chopped ginger and a vinaigrette."}, | |
| {"name": "Pepperoni Pizza", "ingredients": ["Peperoni", "Tomato", "Onion"], "instructions": "Top pizza dough with tomato sauce, peperoni, and onion slices. Bake until crispy."}, | |
| {"name": "Onion Soup", "ingredients": ["Onion"], "instructions": "Sauté onions until caramelized, then add broth and simmer."}, | |
| {"name": "Tomato Salad", "ingredients": ["Tomato", "Onion"], "instructions": "Chop tomatoes and onions, mix with olive oil and vinegar."}, | |
| {"name": "Carrot Ginger Soup", "ingredients": ["Carrot", "Ginger"], "instructions": "Boil carrots and ginger, blend into a creamy soup."}, | |
| {"name": "Potato Jalapeno Gratin", "ingredients": ["Potato", "Jalapeno"], "instructions": "Layer sliced potatoes and jalapenos, bake with cream and cheese."}, | |
| {"name": "Garlic Ginger Stir Fry", "ingredients": ["Garlic", "Ginger"], "instructions": "Stir fry garlic and ginger with your choice of vegetables."}, | |
| {"name": "Roasted Peperoni", "ingredients": ["Peperoni"], "instructions": "Roast whole peperoni in the oven until charred."}, | |
| {"name": "Sweetpotato Fries", "ingredients": ["Sweetpotato"], "instructions": "Cut sweetpotatoes into fries, season, and bake until crispy."}, | |
| {"name": "Garlic Ginger Chicken", "ingredients": ["Garlic", "Ginger"], "instructions": "Marinate chicken with garlic and ginger, then bake or grill."}, | |
| {"name": "Onion Rings", "ingredients": ["Onion"], "instructions": "Dip onion slices in batter and deep fry until golden."}, | |
| {"name": "Tomato Basil Bruschetta", "ingredients": ["Tomato"], "instructions": "Top toasted bread with diced tomatoes, basil, and olive oil."}, | |
| {"name": "Jalapeno Poppers", "ingredients": ["Jalapeno"], "instructions": "Stuff jalapenos with cheese, bread them, and bake or fry."}, | |
| {"name": "Carrot Sweetpotato Mash", "ingredients": ["Carrot", "Sweetpotato"], "instructions": "Boil carrots and sweetpotatoes, then mash with butter and seasoning."} | |
| ] | |
| matching_recipes = [recipe for recipe in sample_recipes if all(item in recipe["ingredients"] for item in ingredients)] | |
| return matching_recipes | |
| matching_recipes = find_recipes(st.session_state.ingredients) | |
| if matching_recipes: | |
| st.write("### Matching Recipes") | |
| for recipe in matching_recipes: | |
| st.write(f"**{recipe['name']}**") | |
| st.write(", ".join(recipe["ingredients"])) | |
| st.write(f"Instructions: {recipe['instructions']}") | |
| else: | |
| st.write("No matching recipes found.") | |
| # Reset button to start over | |
| if st.button("Reset"): | |
| st.session_state.ingredients = [] | |
| st.write("Ingredients list has been reset.") | |