app.py

# -*- coding: utf-8 -*-
"""app.py.ipynb

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1AO89EnPiFQ-JnEpNKwsABc0bj5or_Upn
"""

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
import gradio as gr
import warnings
warnings.filterwarnings('ignore')

# Step 1: Generate dataset and train model
np.random.seed(42)
moods = ['happy', 'stressed', 'bored', 'sad', 'excited', 'tired', 'anxious', 'content', 'nostalgic', 'hungry']
snacks = [
    'fruit', 'chocolate', 'chips', 'popcorn', 'ice cream', 'pretzels', 'cookies', 'candy',
    'yogurt', 'granola bar', 'crackers', 'veggies', 'cheese',
    'chin chin', 'kuli kuli', 'plantain chips', 'puff puff', 'akara', 'coconut candy',
    'kokoro', 'dodo ikire', 'roasted groundnuts', 'suya', 'boli', 'kilishi',
    'buns', 'doughnuts', 'meat pie', 'egg rolls'
]
times_of_day = ['morning', 'afternoon', 'evening', 'midnight']

# Snack groups
snack_groups = {
    'nigerian_fried': ['chin chin', 'puff puff', 'akara', 'buns', 'doughnuts', 'meat pie', 'egg rolls'],
    'nigerian_savory': ['suya', 'kuli kuli', 'plantain chips', 'boli', 'kilishi', 'roasted groundnuts'],
    'nigerian_sweet': ['coconut candy', 'dodo ikire', 'chocolate', 'candy', 'cookies', 'ice cream'],
    'savory_snacks': ['chips', 'popcorn', 'pretzels', 'crackers', 'kokoro'],
    'healthy_light': ['fruit', 'yogurt', 'veggies', 'granola bar', 'cheese']
}
snack_to_group = {snack: group for group, snacks in snack_groups.items() for snack in snacks}
group_list = list(snack_groups.keys())

# Mood-time-snack group affinities
mood_time_group_probs = {
    'happy': {
        'morning': {'nigerian_fried': 0.75, 'nigerian_sweet': 0.2, 'healthy_light': 0.05},
        'afternoon': {'nigerian_fried': 0.75, 'nigerian_sweet': 0.2, 'healthy_light': 0.05},
        'evening': {'nigerian_sweet': 0.75, 'healthy_light': 0.2, 'savory_snacks': 0.05},
        'midnight': {'nigerian_sweet': 0.75, 'nigerian_savory': 0.2, 'savory_snacks': 0.05}
    },
    'stressed': {
        'morning': {'nigerian_sweet': 0.75, 'nigerian_fried': 0.2, 'healthy_light': 0.05},
        'afternoon': {'nigerian_sweet': 0.75, 'nigerian_fried': 0.2, 'savory_snacks': 0.05},
        'evening': {'nigerian_sweet': 0.75, 'savory_snacks': 0.2, 'nigerian_savory': 0.05},
        'midnight': {'nigerian_sweet': 0.75, 'nigerian_savory': 0.2, 'savory_snacks': 0.05}
    },
    'bored': {
        'morning': {'savory_snacks': 0.75, 'nigerian_fried': 0.2, 'healthy_light': 0.05},
        'afternoon': {'savory_snacks': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05},
        'evening': {'savory_snacks': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05},
        'midnight': {'savory_snacks': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05}
    },
    'sad': {
        'morning': {'nigerian_sweet': 0.75, 'nigerian_fried': 0.2, 'healthy_light': 0.05},
        'afternoon': {'nigerian_sweet': 0.75, 'healthy_light': 0.2, 'nigerian_fried': 0.05},
        'evening': {'nigerian_sweet': 0.75, 'healthy_light': 0.2, 'savory_snacks': 0.05},
        'midnight': {'nigerian_sweet': 0.75, 'healthy_light': 0.2, 'nigerian_savory': 0.05}
    },
    'excited': {
        'morning': {'nigerian_fried': 0.75, 'nigerian_sweet': 0.2, 'healthy_light': 0.05},
        'afternoon': {'nigerian_fried': 0.75, 'nigerian_savory': 0.2, 'nigerian_sweet': 0.05},
        'evening': {'nigerian_sweet': 0.75, 'nigerian_savory': 0.2, 'savory_snacks': 0.05},
        'midnight': {'nigerian_savory': 0.75, 'nigerian_sweet': 0.2, 'savory_snacks': 0.05}
    },
    'tired': {
        'morning': {'healthy_light': 0.75, 'nigerian_fried': 0.2, 'nigerian_sweet': 0.05},
        'afternoon': {'healthy_light': 0.75, 'nigerian_fried': 0.2, 'savory_snacks': 0.05},
        'evening': {'healthy_light': 0.75, 'nigerian_sweet': 0.2, 'savory_snacks': 0.05},
        'midnight': {'healthy_light': 0.75, 'nigerian_savory': 0.2, 'nigerian_sweet': 0.05}
    },
    'anxious': {
        'morning': {'savory_snacks': 0.75, 'nigerian_fried': 0.2, 'healthy_light': 0.05},
        'afternoon': {'savory_snacks': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05},
        'evening': {'savory_snacks': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05},
        'midnight': {'savory_snacks': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05}
    },
    'content': {
        'morning': {'healthy_light': 0.75, 'nigerian_fried': 0.2, 'nigerian_sweet': 0.05},
        'afternoon': {'nigerian_savory': 0.75, 'healthy_light': 0.2, 'nigerian_fried': 0.05},
        'evening': {'healthy_light': 0.75, 'nigerian_sweet': 0.2, 'savory_snacks': 0.05},
        'midnight': {'healthy_light': 0.75, 'nigerian_savory': 0.2, 'nigerian_sweet': 0.05}
    },
    'nostalgic': {
        'morning': {'nigerian_sweet': 0.75, 'nigerian_fried': 0.2, 'healthy_light': 0.05},
        'afternoon': {'nigerian_sweet': 0.75, 'nigerian_fried': 0.2, 'healthy_light': 0.05},
        'evening': {'nigerian_sweet': 0.75, 'healthy_light': 0.2, 'savory_snacks': 0.05},
        'midnight': {'nigerian_sweet': 0.75, 'nigerian_savory': 0.2, 'healthy_light': 0.05}
    },
    'hungry': {
        'morning': {'nigerian_fried': 0.75, 'savory_snacks': 0.2, 'healthy_light': 0.05},
        'afternoon': {'nigerian_savory': 0.75, 'nigerian_fried': 0.2, 'savory_snacks': 0.05},
        'evening': {'nigerian_savory': 0.75, 'savory_snacks': 0.2, 'nigerian_sweet': 0.05},
        'midnight': {'nigerian_savory': 0.75, 'savory_snacks': 0.2, 'nigerian_sweet': 0.05}
    }
}

# Generate 1800 samples
n_samples = 1800
data = {'mood': [], 'time_of_day': [], 'hunger_level': [], 'sentiment': [], 'snack': [], 'snack_group': []}

for _ in range(n_samples):
    mood = np.random.choice(moods, p=[0.15, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.05])
    time = np.random.choice(times_of_day)
    hunger_level = 1.0 if mood == 'hungry' else np.random.uniform(0, 0.8)
    sentiment = round(np.random.uniform(-1, 1), 2)

    group_probs = [mood_time_group_probs[mood][time].get(g, 0.01) for g in group_list]
    group = np.random.choice(group_list, p=group_probs / np.sum(group_probs))
    group_snacks = snack_groups[group]
    snack_probs = [
        0.6 if (snack == 'suya' and time in ['evening', 'midnight']) or (snack == 'boli' and time == 'afternoon') or
               (snack in ['puff puff', 'buns', 'doughnuts', 'meat pie', 'egg rolls'] and time in ['morning', 'afternoon']) or
               (snack == 'akara' and time in ['morning', 'midnight']) or
               (snack == 'chin chin' and time in ['morning', 'afternoon', 'midnight'])
        else 0.35 if snack in ['kuli kuli', 'plantain chips', 'popcorn', 'kokoro', 'roasted groundnuts', 'kilishi']
        else 0.2 for snack in group_snacks
    ]
    if time not in ['evening', 'midnight'] and 'suya' in group_snacks:
        snack_probs[group_snacks.index('suya')] = 0
    if time != 'afternoon' and 'boli' in group_snacks:
        snack_probs[group_snacks.index('boli')] = 0
    if time not in ['morning', 'afternoon']:
        for snack in ['puff puff', 'buns', 'doughnuts', 'meat pie', 'egg rolls']:
            if snack in group_snacks:
                snack_probs[group_snacks.index(snack)] = 0
    if time not in ['morning', 'midnight'] and 'akara' in group_snacks:
        snack_probs[group_snacks.index('akara')] = 0
    snack_probs = [p / sum(snack_probs) if sum(snack_probs) > 0 else 0.2 for p in snack_probs]
    snack = np.random.choice(group_snacks, p=snack_probs)

    data['mood'].append(mood)
    data['time_of_day'].append(time)
    data['hunger_level'].append(hunger_level)
    data['sentiment'].append(sentiment)
    data['snack'].append(snack)
    data['snack_group'].append(group)

df = pd.DataFrame(data)

# Adjust sentiment
df.loc[df['mood'].isin(['happy', 'excited', 'content', 'nostalgic']), 'sentiment'] = df.loc[
    df['mood'].isin(['happy', 'excited', 'content', 'nostalgic']), 'sentiment'].clip(lower=0.2)
df.loc[df['mood'].isin(['stressed', 'sad', 'anxious', 'tired']), 'sentiment'] = df.loc[
    df['mood'].isin(['stressed', 'sad', 'anxious', 'tired']), 'sentiment'].clip(upper=-0.1)
df.loc[df['mood'].isin(['bored', 'hungry']), 'sentiment'] = df.loc[
    df['mood'].isin(['bored', 'hungry']), 'sentiment'].clip(-0.3, 0.3)

# Add snack_type and snack_texture
snack_types = {
    'chin chin': 'sweet', 'puff puff': 'sweet', 'akara': 'savory', 'suya': 'spicy',
    'kuli kuli': 'spicy', 'plantain chips': 'savory', 'coconut candy': 'sweet',
    'dodo ikire': 'sweet', 'roasted groundnuts': 'savory', 'fruit': 'light',
    'yogurt': 'light', 'veggies': 'light', 'granola bar': 'light', 'cheese': 'light',
    'chocolate': 'sweet', 'candy': 'sweet', 'cookies': 'sweet', 'ice cream': 'sweet',
    'chips': 'savory', 'popcorn': 'savory', 'pretzels': 'savory', 'crackers': 'savory',
    'kokoro': 'savory', 'boli': 'savory', 'kilishi': 'spicy',
    'buns': 'sweet', 'doughnuts': 'sweet', 'meat pie': 'savory', 'egg rolls': 'savory'
}
snack_textures = {
    'chin chin': 'crisp', 'puff puff': 'soft', 'akara': 'soft', 'suya': 'chewy',
    'kuli kuli': 'crisp', 'plantain chips': 'crisp', 'coconut candy': 'chewy',
    'dodo ikire': 'soft', 'roasted groundnuts': 'crisp', 'fruit': 'soft',
    'yogurt': 'soft', 'veggies': 'crisp', 'granola bar': 'crisp', 'cheese': 'soft',
    'chocolate': 'soft', 'candy': 'chewy', 'cookies': 'crisp', 'ice cream': 'soft',
    'chips': 'crisp', 'popcorn': 'crisp', 'pretzels': 'crisp', 'crackers': 'crisp',
    'kokoro': 'crisp', 'boli': 'soft', 'kilishi': 'chewy',
    'buns': 'soft', 'doughnuts': 'soft', 'meat pie': 'soft', 'egg rolls': 'soft'
}
df['snack_type'] = df['snack'].map(snack_types)
df['snack_texture'] = df['snack'].map(snack_textures)

# Encode features
le_mood = LabelEncoder()
le_time = LabelEncoder()
le_type = LabelEncoder()
le_texture = LabelEncoder()
le_group = LabelEncoder()

df['mood_encoded'] = le_mood.fit_transform(df['mood'])
df['time_encoded'] = le_time.fit_transform(df['time_of_day'])
df['type_encoded'] = le_type.fit_transform(df['snack_type'])
df['texture_encoded'] = le_texture.fit_transform(df['snack_texture'])
df['group_encoded'] = le_group.fit_transform(df['snack_group'])

X = df[['mood_encoded', 'time_encoded', 'hunger_level', 'sentiment', 'type_encoded', 'texture_encoded']]
y = df['group_encoded']

X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)

# Train model
param_grid = {
    'n_estimators': [300, 400],
    'max_depth': [12, 15],
    'min_samples_split': [5, 10]
}
model = RandomForestClassifier(class_weight='balanced', random_state=42)
grid_search = GridSearchCV(model, param_grid, cv=5, scoring='accuracy', n_jobs=-1)
grid_search.fit(X_train, y_train)
best_model = grid_search.best_estimator_

# Prediction function with error handling
def predict_snack(mood, time_of_day, hunger_level, sentiment, snack_type):
    mood_enc = le_mood.transform([mood])[0]
    time_enc = le_time.transform([time_of_day])[0]
    type_enc = le_type.transform([snack_type])[0]
    type_to_texture = {'sweet': 'soft', 'savory': 'crisp', 'spicy': 'chewy', 'light': 'soft'}
    texture_enc = le_texture.transform([type_to_texture[snack_type]])[0]
    input_data = np.array([[mood_enc, time_enc, hunger_level, sentiment, type_enc, texture_enc]])
    pred = best_model.predict(input_data)
    group = le_group.inverse_transform(pred)[0]
    group_snacks = snack_groups[group]
    snack_probs = [
        0.6 if (snack == 'suya' and time_of_day in ['evening', 'midnight']) or
               (snack == 'boli' and time_of_day == 'afternoon') or
               (snack in ['puff puff', 'buns', 'doughnuts', 'meat pie', 'egg rolls'] and time_of_day in ['morning', 'afternoon']) or
               (snack == 'akara' and time_of_day in ['morning', 'midnight']) or
               (snack == 'chin chin' and time_of_day in ['morning', 'afternoon', 'midnight'])
        else 0.35 if snack in ['kuli kuli', 'plantain chips', 'popcorn', 'kokoro', 'roasted groundnuts', 'kilishi']
        else 0.2 for snack in group_snacks
    ]
    # Only modify probabilities if the snack is in the group
    if time_of_day not in ['evening', 'midnight'] and 'suya' in group_snacks:
        snack_probs[group_snacks.index('suya')] = 0
    if time_of_day != 'afternoon' and 'boli' in group_snacks:
        snack_probs[group_snacks.index('boli')] = 0
    if time_of_day not in ['morning', 'afternoon']:
        for snack in ['puff puff', 'buns', 'doughnuts', 'meat pie', 'egg rolls']:
            if snack in group_snacks:
                snack_probs[group_snacks.index(snack)] = 0
    if time_of_day not in ['morning', 'midnight'] and 'akara' in group_snacks:
        snack_probs[group_snacks.index('akara')] = 0
    snack_probs = [p / sum(snack_probs) if sum(snack_probs) > 0 else 0.2 for p in snack_probs]
    snack = np.random.choice(group_snacks, p=snack_probs)
    return f"You're craving: {snack} (from {group})!"

# Gradio interface
interface = gr.Interface(
    fn=predict_snack,
    inputs=[
        gr.Dropdown(choices=moods, label="Mood", value="happy"),
        gr.Dropdown(choices=times_of_day, label="Time of Day", value="morning"),
        gr.Slider(minimum=0, maximum=1, step=0.1, label="Hunger Level (0 to 1)", value=0.5),
        gr.Slider(minimum=-1, maximum=1, step=0.1, label="Sentiment (-1 to 1)", value=0.0),
        gr.Dropdown(choices=['sweet', 'savory', 'spicy', 'light'], label="Snack Type", value="sweet")
    ],
    outputs=gr.Textbox(label="Prediction"),
    title="Snack Predictor",
    description="Discover your perfect snack based on your mood,time of the day and preferences!"
)

# Launch the app
interface.launch(share=True)