Create app.py

app.py

@@ -0,0 +1,261 @@
+# -*- 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)