Spaces:
Sleeping
Sleeping
File size: 16,352 Bytes
748b2eb b5194d6 748b2eb 1f6b20c 748b2eb 1f6b20c 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 082307a 748b2eb 729b8a5 082307a 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 748b2eb 729b8a5 b5194d6 1f6b20c b5194d6 1f6b20c b5194d6 1f6b20c 729b8a5 1f6b20c 729b8a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 |
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 os
import warnings
import logging
warnings.filterwarnings('ignore')
logging.basicConfig(level=logging.INFO)
# Dataset generation
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 = {
'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_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}
}
}
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[str(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
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
]
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 should try {snack}!", snack
# Gradio interface
with gr.Blocks(css="""
body {background-color: #FFF8E7; font-family: 'Poppins', sans-serif;}
.gradio-container {max-width: 800px; margin: auto; padding: 20px;}
h1 {color: #4A2C2A; text-align: center; font-size: 2.5em; margin-bottom: 10px;}
p {color: #4A2C2A; text-align: center; font-size: 1.2em;}
.gr-button {background-color: #FF4500 !important; color: white !important; border-radius: 25px !important; padding: 10px 20px !important; font-weight: bold !important;}
.gr-button:hover {background-color: #E03C00 !important;}
.gr-textbox, .gr-dropdown, .gr-slider {border: 2px solid #D4A373 !important; border-radius: 10px !important; padding: 10px !important;}
.gr-image {border-radius: 15px; margin: auto; max-width: 200px;}
.footer {text-align: center; color: #808080; font-size: 0.9em; margin-top: 20px;}
""") as demo:
gr.HTML("""
<div style='background: linear-gradient(to right, #D4A373, #FEE440); padding: 20px; border-radius: 15px; text-align: center;'>
<h1>Snack Predictor πͺ</h1>
<p>Tell us your vibe, and we'll find your perfect snack! Powered by ML (~97% accurate)</p>
</div>
""")
gr.HTML("<p style='text-align: center; color: #4A2C2A; margin-top: 15px;'>Welcome to your snack adventure! π</p>")
with gr.Row():
with gr.Column(scale=1):
mood = gr.Dropdown(choices=moods, label="Mood", value="happy", elem_classes="gr-dropdown")
time_of_day = gr.Dropdown(choices=times_of_day, label="Time of Day", value="morning", elem_classes="gr-dropdown")
hunger_level = gr.Slider(minimum=0, maximum=1, step=0.1, label="Hunger Level (0 to 1)", value=0.5, elem_classes="gr-slider")
sentiment = gr.Slider(minimum=-1, maximum=1, step=0.1, label="Sentiment (-1 to 1)", value=0.0, elem_classes="gr-slider")
snack_type = gr.Dropdown(choices=['sweet', 'savory', 'spicy', 'light'], label="Snack Type", value="sweet", elem_classes="gr-dropdown")
predict_btn = gr.Button("Find My Snack!", variant="primary", elem_classes="gr-button")
with gr.Column(scale=1):
output_text = gr.Textbox(label="Your Snack Recommendation", elem_classes="gr-textbox")
output_image = gr.Image(label="Snack Preview", elem_classes="gr-image")
def predict_and_show(mood, time_of_day, hunger_level, sentiment, snack_type):
text, snack = predict_snack(mood, time_of_day, hunger_level, sentiment, snack_type)
image_path = f"assets/{snack.replace(' ', '_')}.jpeg"
if not os.path.exists(image_path):
logging.info(f"Image not found: {image_path}")
image_path = f"assets/{snack.replace(' ', '_')}.png" # Check for .png as fallback
if not os.path.exists(image_path):
logging.info(f"PNG fallback not found: {image_path}, using placeholder")
image_path = "assets/placeholder.jpeg" # Final fallback
if not os.path.exists(image_path):
logging.error(f"Placeholder not found: {image_path}")
return text, image_path
predict_btn.click(
fn=predict_and_show,
inputs=[mood, time_of_day, hunger_level, sentiment, snack_type],
outputs=[output_text, output_image]
)
gr.HTML("""
<div class='footer'>
<p>Built with β€οΈ by @teganmosi π</p>
<p>Follow my #WeeklyMLProjects for more! π</p>
</div>
""")
demo.launch() |